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Математическая статистика-1 2025/26 (пилотный поток)

2026-04-09T16:34:34Z

Fedor.Noskov: группа 243 -- сслыки

== Преподаватели и учебные ассистенты ==
{| class="wikitable" style="text-align:center"
|-
! Группа !! БПМИ241 !! БПМИ242 !! БПМИ243 !! БПМИ244 !! БПМИ245
|-
|| Лектор ||colspan="5"| [https://t.me/nikita_puchkin Пучкин Никита Андреевич]
|-
|| Ассистент курса ||colspan="5"| [https://t.me/lipperdino Анна Маркович]
|-
|| Семинарист || [https://t.me/nikita_puchkin Никита Пучкин] || [https://t.me/Bicycle_Standart Константин Яковлев] || [https://t.me/teddy_nos Федор Носков] || [https://t.me/sheshamar Марина Шешукова] || [https://t.me/spankevich Сергей Панкевич]
|-
|| Ассистенты || [https://t.me/burguty Никита Севрюков] [https://t.me/trsh_k Екатерина Трушкова] || [https://t.me/Agafonov_Artem Артем Агафонов] [https://t.me/ArtemRubtsov2003 Артемий Рубцов] || [https://t.me/rualss Алексей Рутковский] [https://t.me/ya_veraaa Вера Слипченко] || [https://t.me/daaa_k Дарья Коровайцева] [https://t.me/mirusanova Маргарита Русанова] || [https://t.me/akveronika Вероника Аксененко] [https://t.me/dasssshhkk Дарья Мамонтова]
|-
|| Группа в Telegram || [https://t.me/+LPIIGAkGaxQyM2Ey Группа 241] || [ Группа 242] || [https://t.me/+xuaBhIgPR2BmY2Fi Группа 243] || [https://t.me/+5CLhyNTLrqAxMmM6 Группа 244] || [https://t.me/+Ry3FfOI7tTc4OTky Группа 245]
|-
|colspan="6"| [https://t.me/+DwV4tfHltT5lZmFi Чат пилотного потока в Telegram]
|-
|colspan="6"| [https://t.me/+mIEHdpLYyAw4NGMy Канал пилотного потока в Telegram]
|}

== Организационные моменты ==
=== Правила игры ===
Оценка за курс вычисляется по формуле

Оитог = 0.2 * ТДЗ + 0.15 * ПДЗ + 0.25 * КР + 0.4 * Э,

где

* ТДЗ — оценка за теоретические домашние задания;
* ПДЗ — оценка за практические домашние задания;
* КР — оценка за контрольную работу;
* Э — оценка за письменный экзамен.

Округляется только итоговый балл. Правило округления стандартное (арифметическое).

=== Использование генеративных моделей ===
Использование генеративных моделей (ChatGPT, Copilot и т.д.) при решении домашних заданий не запрещено при условии явного указания, какие части задания были выполнены с помощью генеративных моделей. Копирование ответов генеративных моделей без упоминания их использования приравнивается к списыванию и влечет обнуление баллов за домашнее задание и подачу служебной записки в деканат. При повторном списывании деканат имеет право отчислить студента.

=== Ведомость с оценками ===

{| class="wikitable" style="text-align:center"
|-
! [ 241] !! [ 242] !! [ 243] !! [ 244] !! [ 245]
|}

=== Ссылки на классрум ===

{| class="wikitable" style="text-align:center"
|-
! [https://classroom.google.com/c/ODU4NzY5NTgzMTk1?cjc=ntw2pkia 241] !! [ 242 ] !! [https://classroom.google.com/c/ODE5NDcxODk4OTE4?cjc=cpmfwdm7 243] !! [https://classroom.google.com/c/ODU4ODI3NTM5NzE1?cjc=tlw7y6ph 244] !! [ 245]
|}

== Материалы ==

[https://disk.yandex.ru/i/tqXptDhnGBincQ '''Обновляемый конспект лекций''']

[https://disk.yandex.ru/i/ADxIbzveKwPsTg '''Конспект лекций прошлого года''']

[https://disk.yandex.ru/d/w-2Ir_OIvohc7A '''Семинары группы 241 (листочки)''']

== Домашние задания ==

{| class="wikitable" style="text-align:center"
|-
! Теоретические домашние задания || Практические домашние задания
|-
! [https://disk.yandex.ru/i/NpYdG5xg_ec9Lg ТДЗ 1, дедлайн - 16.04, 23:59] || [ ПДЗ 1, дедлайн - XX.04, 23:59]
|}

Каждому студенту даётся 5 дней отсрочки на все домашние задания, которые можно потратить как удобно (можно, например, потратить все на одно ДЗ или на три разных). Также есть 3 микроотсрочки на 15 минут. Несданное или сданное слишком поздно (в этом случае оно оценивается нулём баллов) ДЗ не расходует дни отсрочки.

== Список рекомендуемой литературы ==
=== Учебники ===
* Ивченко Г. И., Медведев Ю. И., "Введение в математическую статистику"

* Лагутин М. Б., "Наглядная математическая статистика"

* Бородин А. Н., "Элементарный курс теории вероятностей и математической статистики"

* Боровков А. А., "Математическая статистика"

* Wasserman L. A. [https://egrcc.github.io/docs/math/all-of-statistics.pdf All of Statistics: A Concise Course in Statistical Inference]

=== Задачники ===
* Коршунов Д. А., Чернова Н. И., "Сборник задач и упражнений по математической статистике"

== Страницы прошлых лет ==

[http://wiki.cs.hse.ru/Математическая_статистика_2024/25_(пилотный_поток) 2024/2025 учебный год]

[http://wiki.cs.hse.ru/Математическая_статистика_2023/24_(пилотный_поток) 2023/2024 учебный год]

[http://wiki.cs.hse.ru/Математическая_статистика_2022/2023_(пилотный_поток) 2022/2023 учебный год]

High-dimensional Probability and Statistics (2026)

2026-02-27T10:11:11Z

Fedor.Noskov:

= Classes =

Wednesdays 14:40–17:40, in room G410.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (21.01.2026) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* (28.01.2026) Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* (04.02.2026) Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* (11.02.2026) Chapter 2.2 from [[#wainwright|[Wainwright]]]. Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* (18.02.2026) Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]]. KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]).

* Concentration of Gaussian Lipschitz functions.

* Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

= Grading =

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

= Midterm =

TBA

= Home assignments =

TBA

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2026)

2026-02-07T22:24:50Z

Fedor.Noskov:

= Classes =

Wednesdays 14:40–17:40, in room G410.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (21.01.2026) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* (28.01.2026) Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* (04.02.2026) Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* Chapter 2.2 from [[#wainwright|[Wainwright]]].

* Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

= Grading =

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

= Midterm =

TBA

= Home assignments =

TBA

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2026)

2026-01-22T13:20:42Z

Fedor.Noskov:

= Classes =

Wednesdays 14:40–17:40, in room G410.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (21.01.2026) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* Chapter 2.2 from [[#wainwright|[Wainwright]]].

* Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

= Grading =

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

= Midterm =

TBA

= Home assignments =

TBA

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2026)

2026-01-22T13:20:13Z

Fedor.Noskov:

= Classes =

Wednesdays 14:40–17:40, in room G410.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* Chapter 2.2 from [[#wainwright|[Wainwright]]].

* Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

= Grading =

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

= Midterm =

TBA

= Home assignments =

TBA

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2026)

2026-01-22T13:19:52Z

Fedor.Noskov:

= Classes =

Wednesdays 14:40–17:40, in room G410.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* Chapter 2.2 from [[#wainwright|[Wainwright]]].

* Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

= Grading =

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

= Midterm =

= Home assignments =

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2026)

2026-01-22T13:19:21Z

Fedor.Noskov: создание страницы курса

= Classes =

Wednesdays 14:40–17:40, in room G410.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* Chapter 2.2 from [[#wainwright|[Wainwright]]].

* Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

== Grading ==

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

== Midterm ==

== Home assignments ==

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2025)

2025-06-04T16:46:08Z

Fedor.Noskov:

= Classes =

Wednesdays 13:00–16:00, in room G110.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (15.01.24) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* (22.01.24) Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* (29.01.24) Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* (05.02.24) Chapter 2.2 from [[#wainwright|[Wainwright]]].

* (12.02.24) Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* (19.02.24) Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* (26.02.24) KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* (05.03.24) Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* (12.03.24) Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* (17.03.24) Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

== Grading ==

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

== Midterm ==

See the midterm program [https://disk.yandex.ru/i/Xd6yWNDGpXRu8Q here].

== Home assignments ==

Please, send your solutions to [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2?cjc=h6qefgx Google classroom].

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzQ5NjY1OTM0Nzg0/details Optional home assignment I]. The deadline is April, 6, 23:59.

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzYyNTY4MzE1OTU3/details Obligatory home assignment I]. The deadline is March 30, 23:59.

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzY2NzM4MTgxNTIz/details Obligatory home assignment II]. The deadline is June 22, 23:59.

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

Математическая статистика 2024/25 (пилотный поток)

2025-04-20T16:26:50Z

Fedor.Noskov:

== Преподаватели и учебные ассистенты ==
{| class="wikitable" style="text-align:center"
|-
! Группа !! БПМИ231 !! БПМИ232 !! БПМИ233 !! БПМИ234
|-
|| Лектор ||colspan="4"| [https://t.me/nikita_puchkin Пучкин Никита Андреевич]
|-
|| Ассистент курса ||colspan="4"| [https://t.me/horbachmp Марина Горбач]
|-
|| Семинарист || [https://t.me/ssamsonov Сергей Самсонов] || [https://t.me/sheshamar Марина Шешукова] || [https://t.me/teddy_nos Федор Носков] || [https://t.me/nikita_puchkin Никита Пучкин]
|-
|| Ассистенты || [https://t.me/artempotarusov Артем Потарусов] [https://t.me/katasonov_eugene Катасонов Евгений] || [https://t.me/lipperdino Анна Маркович] [https://t.me/sumimmanis Игнат Сальников] || [https://t.me/Alexxxey5 Алексей Воронко] [https://t.me/@GRIGORYZL Григорий Злотин] || [https://t.me/set_and_sad Илья Вдовец] [https://t.me/volyachka Ольга Турчина]
|-
|| Группа в Telegram || [https://t.me/+I2Re_c3ztsw5ZDNi Группа 231] || [https://t.me/+E3Ls7-nioV43ZjBi Группа 232] || [https://t.me/+2zVzgaDP9YBmZjhi Группа 233] || [https://t.me/+ZmpZ__IpYeZlZjRi Группа 234]
|-
|colspan="5"| [https://t.me/+wag3lT12-nA2MTdi Чат пилотного потока в Telegram]
|}

== Организационные моменты ==
=== Правила игры ===
Оценка за курс вычисляется по формуле

Оитог = 0.2 * ТДЗ + 0.15 * ПДЗ + 0.25 * КР + 0.4 * Э,

где

* ТДЗ — оценка за теоретические домашние задания;
* ПДЗ — оценка за практические домашние задания;
* КР — оценка за контрольную работу;
* Э — оценка за письменный экзамен.

Округляется только итоговый балл. Правило округления стандартное (арифметическое).

=== Ссылки на классрум ===

{| class="wikitable" style="text-align:center"
|-
! 231 !! [https://classroom.google.com/c/NzcxNTMyNDc0NDE4?cjc=ipmlyvci 232] !! [https://classroom.google.com/c/NzcxNTY2MTQ0MjUx?cjc=u273jxpd 233] !! [https://classroom.google.com/c/NzcyNjYyOTUzNjkz?cjc=2o6u7yia 234]
|}

== Материалы ==

[https://disk.yandex.ru/i/ADxIbzveKwPsTg '''Обновляемый конспект лекций''']

[https://disk.yandex.ru/i/F5nTjQH9AFZqXQ '''Конспект лекций прошлого года''']

[https://disk.yandex.ru/d/7DiA0VydpCMZug '''Семинары группы 231''']

[https://disk.yandex.ru/d/mKJRIrmQSnIKzg '''Семинары группы 234''']

== Домашние задания ==

{| class="wikitable" style="text-align:center"
|-
! [https://disk.yandex.ru/i/se85QkgOgPFXOA Домашнее задание №1, дедлайн - 20.04, 23:59]
|}

Каждому студенту даётся 5 дней отсрочки на все домашние задания, которые можно потратить как удобно (можно, например, потратить все на одно ДЗ или на три разных). Также есть 3 микроотсрочки на 15 минут. Несданное или сданное слишком поздно (в этом случае оно оценивается нулём баллов) ДЗ не расходует дни отсрочки.

== Список рекомендуемой литературы ==
=== Учебники ===
* Ивченко Г. И., Медведев Ю. И. [https://disk.yandex.ru/i/waXgDQWDh_rgTA Введение в математическую статистику ]

* Лагутин М. Б. [http://iosipoi.com/teachingfiles/stat/Lagutin.pdf Наглядная математическая статистика]

* Бородин А. Н. [https://disk.yandex.ru/i/Ubk5YLMk_PJjYw Элементарный курс теории вероятностей и математической статистики]

* Боровков А. А. [https://disk.yandex.ru/i/212K-4gWWwjQzA Математическая статистика]

* Wasserman L. A. [https://egrcc.github.io/docs/math/all-of-statistics.pdf All of Statistics: A Concise Course in Statistical Inference]

=== Задачники ===
* Коршунов Д. А., Чернова Н. И. [https://disk.yandex.ru/i/TB9dxbQ1Nz1JyA Сборник задач и упражнений по математической статистике]

== Страницы прошлых лет ==

[http://wiki.cs.hse.ru/Математическая_статистика_2023/24_(пилотный_поток) 2023/2024 учебный год]

[http://wiki.cs.hse.ru/Математическая_статистика_2022/2023_(пилотный_поток) 2022/2023 учебный год]

Математическая статистика 2024/25 (пилотный поток)

2025-04-11T23:01:44Z

Fedor.Noskov: добавил чат

== Преподаватели и учебные ассистенты ==
{| class="wikitable" style="text-align:center"
|-
! Группа !! БПМИ231 !! БПМИ232 !! БПМИ233 !! БПМИ234
|-
|| Лектор ||colspan="4"| [https://t.me/nikita_puchkin Пучкин Никита Андреевич]
|-
|| Ассистент курса ||colspan="4"| [https://t.me/horbachmp Марина Горбач]
|-
|| Семинарист || [https://t.me/ssamsonov Сергей Самсонов] || [https://t.me/sheshamar Марина Шешукова] || [https://t.me/teddy_nos Федор Носков] || [https://t.me/nikita_puchkin Никита Пучкин]
|-
|| Ассистенты || [https://t.me/artempotarusov Артем Потарусов] [https://t.me/katasonov_eugene Катасонов Евгений] || [https://t.me/lipperdino Анна Маркович] [https://t.me/sumimmanis Игнат Сальников] || [https://t.me/Alexxxey5 Алексей Воронко] [https://t.me/@GRIGORYZL Григорий Злотин] || [https://t.me/set_and_sad Илья Вдовец] [https://t.me/volyachka Ольга Турчина]
|-
|| Группа в Telegram || [https://t.me/+I2Re_c3ztsw5ZDNi Группа 231] || Группа 232 || [https://t.me/+2zVzgaDP9YBmZjhi Группа 233] || [https://t.me/+ZmpZ__IpYeZlZjRi Группа 234]
|-
|colspan="5"| [https://t.me/+wag3lT12-nA2MTdi Чат пилотного потока в Telegram]
|}

== Организационные моменты ==
=== Правила игры ===
Оценка за курс вычисляется по формуле

Оитог = 0.2 * ТДЗ + 0.15 * ПДЗ + 0.25 * КР + 0.4 * Э,

где

* ТДЗ — оценка за теоретические домашние задания;
* ПДЗ — оценка за практические домашние задания;
* КР — оценка за контрольную работу;
* Э — оценка за письменный экзамен.

Округляется только итоговый балл. Правило округления стандартное (арифметическое).

== Материалы ==

[https://disk.yandex.ru/i/ADxIbzveKwPsTg '''Обновляемый конспект лекций''']

[https://disk.yandex.ru/i/F5nTjQH9AFZqXQ '''Конспект лекций прошлого года''']

[https://disk.yandex.ru/d/mKJRIrmQSnIKzg '''Семинары группы 234''']

== Домашние задания ==

{| class="wikitable" style="text-align:center"
|-
! [https://disk.yandex.ru/i/se85QkgOgPFXOA Домашнее задание №1, дедлайн - 20.04, 23:59]
|}

Каждому студенту даётся 5 дней отсрочки на все домашние задания, которые можно потратить как удобно (можно, например, потратить все на одно ДЗ или на три разных). Также есть 3 микроотсрочки на 15 минут. Несданное или сданное слишком поздно (в этом случае оно оценивается нулём баллов) ДЗ не расходует дни отсрочки.

== Список рекомендуемой литературы ==
=== Учебники ===
* Ивченко Г. И., Медведев Ю. И. [https://disk.yandex.ru/i/waXgDQWDh_rgTA Введение в математическую статистику ]

* Лагутин М. Б. [http://iosipoi.com/teachingfiles/stat/Lagutin.pdf Наглядная математическая статистика]

* Бородин А. Н. [https://disk.yandex.ru/i/Ubk5YLMk_PJjYw Элементарный курс теории вероятностей и математической статистики]

* Боровков А. А. [https://disk.yandex.ru/i/212K-4gWWwjQzA Математическая статистика]

* Wasserman L. A. [https://egrcc.github.io/docs/math/all-of-statistics.pdf All of Statistics: A Concise Course in Statistical Inference]

=== Задачники ===
* Коршунов Д. А., Чернова Н. И. [https://disk.yandex.ru/i/TB9dxbQ1Nz1JyA Сборник задач и упражнений по математической статистике]

== Страницы прошлых лет ==

[http://wiki.cs.hse.ru/Математическая_статистика_2023/24_(пилотный_поток) 2023/2024 учебный год]

[http://wiki.cs.hse.ru/Математическая_статистика_2022/2023_(пилотный_поток) 2022/2023 учебный год]

High-dimensional Probability and Statistics (2025)

2025-03-27T15:26:26Z

Fedor.Noskov: добавлена программа мидтерма

= Classes =

Wednesdays 13:00–16:00, in room G110.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (15.01.24) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* (22.01.24) Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* (29.01.24) Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* (05.02.24) Chapter 2.2 from [[#wainwright|[Wainwright]]].

* (12.02.24) Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* (19.02.24) Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* (26.02.24) KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* (05.03.24) Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* (12.03.24) Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* (17.03.24) Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

== Grading ==

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

== Midterm ==

See the midterm program [https://disk.yandex.ru/i/Xd6yWNDGpXRu8Q here].

== Home assignments ==

Please, send your solutions to [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2?cjc=h6qefgx Google classroom].

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzQ5NjY1OTM0Nzg0/details Optional home assignment I]. The deadline is April, 6, 23:59.

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzYyNTY4MzE1OTU3/details Obligatory home assignment I]. The deadline is March 30, 23:59.

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2025)

2025-03-27T15:22:52Z

Fedor.Noskov: Программа 3 модуля

= Classes =

Wednesdays 13:00–16:00, in room G110.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (15.01.24) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* (22.01.24) Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* (29.01.24) Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* (05.02.24) Chapter 2.2 from [[#wainwright|[Wainwright]]].

* (12.02.24) Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* (19.02.24) Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman |[Weissman]]].

* (26.02.24) KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* (05.03.24) Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* (12.03.24) Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* (17.03.24) Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp |[Tropp]]]). Application to graph sparsifying (Chapter 32 from [[#Spielman | [Spielman]]]).

== Grading ==

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

== Home assignments ==

Please, send your solutions to [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2?cjc=h6qefgx Google classroom].

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzQ5NjY1OTM0Nzg0/details Optional home assignment I]. The deadline is April, 6, 23:59.

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzYyNTY4MzE1OTU3/details Obligatory home assignment I]. The deadline is March 30, 23:59.

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

[Spielman] [https://disk.yandex.ru/i/BIqcuUz1yZb0Cg Daniel Spielman. Spectral and Algebraic Graph Theory]

High-dimensional Probability and Statistics (2025)

2025-03-27T15:18:21Z

Fedor.Noskov:

= Classes =

Wednesdays 13:00–16:00, in room G110.

Teachers: [https://www.hse.ru/en/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (15.01.24) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

* (22.01.24) Chapters 3.6-3.7 from [[#blm|[BLM]]]. For the Sobolev spaces, weak derivatives and the approximation argument, see Chapters 5.2-5.3 [[#EvansPDE | [EvansPDE]]]. See also Chapters 2.1-2.3 of [[#Ziemer | [Ziemer]]].

* (29.01.24) Chapter 2.1.2, Proposition 2.14 from [[#wainwright|[Wainwright]]]. Concentration of order statistics (folklore).

* (05.02.24) Chapter 2.2 from [[#wainwright|[Wainwright]]].

* (12.02.24) Chapter 2.1.3 from [[#wainwright|[Wainwright]]]. Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]].

* (19.02.24) Orlicz norms, see Chapter 2.5 from [[#vershynin|[Vershynin]]]. Sanov's theorem and KL-divergence, Chapter 6.2 from [[#Weissman]|[Weissman]]].

* (26.02.24) KL-divergence. Definition of entropy. Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]). Gaussian Logarithmic Sobolev inequality (Theorem 5.4 from [[#blm|[BLM]]]). Sub-additivity of the entropy (Section 4.13 [[#blm|[BLM]]]). Concentration of Gaussian Lipschitz functions.

* (05.03.24) Uniform Johnson-Lindenstrauss lemma (Theorem 5.10 from [[#blm|[BLM]]]). A modified logarithmic Sobolev inequality (Theorem 6.7 from [[#blm|[BLM]]]).

* (12.03.24) Functional Hoeffding inequality (Theorem 3.26 from [[#wainwright|[Wainwright]]]). Largest eigenvalue of a random symmetric matrix (Example 6.8 from [[#blm|[BLM]]]). Matrix Chernoff bound (Lemma 6.12 from [[#wainwright|[Wainwright]]].

* (17.03.24) Matrix Chernoff from PSD matrices (Theorem 5.1.1 from [[#Tropp]|[Tropp]]]).

== Grading ==

The final grade is obtained as follows:

0.2 HW HDP + 0.3 Midterm HDP + 0.2 HW HDS + 0.3 Exam HDS,

where HW stands for home assignment, HDP stands for High-Dimensional Probability, HDS stands for High-Dimensional Statistics.

== Home assignments ==

Please, send your solutions to [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2?cjc=h6qefgx Google classroom].

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzQ5NjY1OTM0Nzg0/details Optional home assignment I]. The deadline is April, 6, 23:59.

* [https://classroom.google.com/c/NzQ3Njg2NjQ0NTc2/a/NzYyNTY4MzE1OTU3/details Obligatory home assignment I]. The deadline is March 30, 23:59.

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[EvansPDE] [https://disk.yandex.ru/i/cCHL2bbbr0V-7Q Lawrence Evans. Partial Differential Equations]

[Ziemer] [https://disk.yandex.ru/i/1aMzdRyaMZixCQ William Ziemer. Weakly Differentiable Equations]

[Weissman] [https://disk.yandex.ru/i/OPDZenkw-OPSAA Tsachy Weissman. Information theory, Lecture notes]

[Tropp] [https://disk.yandex.ru/i/nzHbi_l_SALkew Joel Tropp. An Introduction to Matrix Concentration Inequalities]

High-dimensional Probability and Statistics (2025)

2025-03-27T15:00:59Z

Fedor.Noskov:

High-dimensional Probability and Statistics (2025)

2025-02-11T14:09:27Z

Fedor.Noskov:

High-dimensional Probability and Statistics (2025)

2025-01-22T17:20:53Z

Fedor.Noskov:

High-dimensional Probability and Statistics (2025)

2025-01-22T16:52:14Z

Fedor.Noskov:

High-dimensional Probability and Statistics (2025)

2025-01-21T16:23:37Z

Fedor.Noskov: создана страница

= Classes =

Wednesdays 13:00–16:00, in room G110.

Teachers: [https://www.hse.ru/org/persons/510369027 Fedor Noskov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture/Seminar content =

=== Probability ===

* (15.01.24) Chapter 3.1, Examples 3.5 and 3.14 from [[#blm|[BLM]]]

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

High-dimensional Probability and Statistics

2024-06-06T13:55:22Z

Fedor.Noskov:

= Classes =

Wednesdays 16:20–17:40, in room R307.

Teachers: [https://www.hse.ru/en/staff/anaumov Alexey Naumov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture content =

* (17.01.24) Chapter 1 from [[#van_handel|[van Handel]]]

= Seminar content =

=== Probability ===

* (17.01.24) Example 2.4 from [[#wainwright|[Wainwright]]], Lemma 2.2 from [[#blm|[BLM]]]

* (24.01.24) Appendix A and Exercise 2.2 of the second chapter of [[#wainwright|[Wainwright]]], Section 2.5.1 from [[#vershynin|[Vershynin]]]

* (31.01.24) Section 2.1.3 and Example 2.12 from [[#wainwright|[Wainwright]]]

* (07.02.24) Section 2.3 from [[#wainwright|[Wainwright]]]

* (24.02.24) Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]), Sub-additivity of the entropy (Theorem 4.22 from [[#blm|[BLM]]]), logorithmic Sobolev inequality for Gaussian random variables (Theorem 5.5 from [[#blm|[BLM]]])

* (07.03.24) PAC-Bayesian inequality. (Lemma 2.1 from [[#zhivotovsky | [Zhivotovsky]]])

* (14.03.24) Dimension-free concentration of sample covariance matrix in the spectral norm (Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]])

* (21.03.24) Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]], Concentration of Lipshitz and separately convex function of bounded random variables (Theorem 6.10 from [[#blm|[BLM]]]), Concentration of the supremum of an empirical process (Section 3.4 of [[#wainwright|[Wainwright]]])

=== Statistics ===

* (10.04.24) Linear Regression. Bayesian information criterion. (Theorem 2.4 from [[#rigollet | [Rigollet]]])

* (17.04.24) Restricted isometry property and epsilon-incoherence. (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]])

* (24.04.24) Incoherence of a random matrix with independent Rademacher entries (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]]). Empirical risk minimization and Rademacher complexity (Sections 4.1-4.2 of [[#wainwright|[Wainwright]]]). Bounds on the Rademacher complexity of finite and finite-dimensional classes can be found in [[#paris | [Paris]]], Theorems 6.1 and 6.3.

* (15.05.24) VC-dimension, Sauer's lemma. (Section 4.3 of [[#wainwright|[Wainwright]]]).

* (22.05.24) Packing-covering duality. (Lemma 5.12 of [[#van_handel|[van Handel]]]). Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

* (29.05.24) Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

* (05.06.24) Offset Rademacher Complexity (some parts of [[#puchkin|[Puchkin]]])

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Zhivotovsky] [https://disk.yandex.ru/i/3yAKKiHc73ZSZQ Nikita Zhivotovskiy. Dimension-free bounds for sums of independent matrices and simple tensors via the variational principle. Electron. J. Probab. vol. 29 (2024), article no. 13, 1–28.]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[Puchkin] [https://proceedings.mlr.press/v195/puchkin23a.html Nikita Puchkin, Nikita Zhivotovskiy. Exploring Local Norms in Exp-concave Statistical Learning. COLT 2023]

High-dimensional Probability and Statistics

2024-06-06T13:52:27Z

Fedor.Noskov:

= Classes =

Wednesdays 16:20–17:40, in room R307.

Teachers: [https://www.hse.ru/en/staff/anaumov Alexey Naumov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture content =

* (17.01.24) Chapter 1 from [[#van_handel|[van Handel]]]

= Seminar content =

=== Probability ===

* (17.01.24) Example 2.4 from [[#wainwright|[Wainwright]]], Lemma 2.2 from [[#blm|[BLM]]]

* (24.01.24) Appendix A and Exercise 2.2 of the second chapter of [[#wainwright|[Wainwright]]], Section 2.5.1 from [[#vershynin|[Vershynin]]]

* (31.01.24) Section 2.1.3 and Example 2.12 from [[#wainwright|[Wainwright]]]

* (07.02.24) Section 2.3 from [[#wainwright|[Wainwright]]]

* (24.02.24) Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]), Sub-additivity of the entropy (Theorem 4.22 from [[#blm|[BLM]]]), logorithmic Sobolev inequality for Gaussian random variables (Theorem 5.5 from [[#blm|[BLM]]])

* (07.03.24) PAC-Bayesian inequality. (Lemma 2.1 from [[#zhivotovsky | [Zhivotovsky]]])

* (14.03.24) Dimension-free concentration of sample covariance matrix in the spectral norm (Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]])

* (21.03.24) Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]], Concentration of Lipshitz and separately convex function of bounded random variables (Theorem 6.10 from [[#blm|[BLM]]]), Concentration of the supremum of an empirical process (Section 3.4 of [[#wainwright|[Wainwright]]])

=== Statistics ===

* (10.04.24) Linear Regression. Bayesian information criterion. (Theorem 2.4 from [[#rigollet | [Rigollet]]])

* (17.04.24) Restricted isometry property and epsilon-incoherence. (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]])

* (24.04.24) Incoherence of a random matrix with independent Rademacher entries (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]]). Empirical risk minimization and Rademacher complexity (Sections 4.1-4.2 of [[#wainwright|[Wainwright]]]). Bounds on the Rademacher complexity of finite and finite-dimensional classes can be found in [[#paris | [Paris]]], Theorems 6.1 and 6.3.

* (15.05.24) VC-dimension, Sauer's lemma. (Section 4.3 of [[#wainwright|[Wainwright]]]).

* (22.05.24) Packing-covering duality. (Lemma 5.12 of [[#van_handel|[van Handel]]]). Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

* (29.05.24) Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

* (05.06.24) Offset Rademacher Complexity (some parts of [[#puchkin|[Puchkin]]]

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Zhivotovsky] [https://disk.yandex.ru/i/3yAKKiHc73ZSZQ Nikita Zhivotovskiy. Dimension-free bounds for sums of independent matrices and simple tensors via the variational principle. Electron. J. Probab. vol. 29 (2024), article no. 13, 1–28.]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[Puchkin] [https://proceedings.mlr.press/v195/puchkin23a.html Nikita Puchkin, Nikita Zhivotovskiy. Exploring Local Norms in Exp-concave Statistical Learning. COLT 2023]

High-dimensional Probability and Statistics

2024-06-06T13:51:59Z

Fedor.Noskov:

= Classes =

Wednesdays 16:20–17:40, in room R307.

Teachers: [https://www.hse.ru/en/staff/anaumov Alexey Naumov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture content =

* (17.01.24) Chapter 1 from [[#van_handel|[van Handel]]]

= Seminar content =

=== Probability ===

* (17.01.24) Example 2.4 from [[#wainwright|[Wainwright]]], Lemma 2.2 from [[#blm|[BLM]]]

* (24.01.24) Appendix A and Exercise 2.2 of the second chapter of [[#wainwright|[Wainwright]]], Section 2.5.1 from [[#vershynin|[Vershynin]]]

* (31.01.24) Section 2.1.3 and Example 2.12 from [[#wainwright|[Wainwright]]]

* (07.02.24) Section 2.3 from [[#wainwright|[Wainwright]]]

* (24.02.24) Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]), Sub-additivity of the entropy (Theorem 4.22 from [[#blm|[BLM]]]), logorithmic Sobolev inequality for Gaussian random variables (Theorem 5.5 from [[#blm|[BLM]]])

* (07.03.24) PAC-Bayesian inequality. (Lemma 2.1 from [[#zhivotovsky | [Zhivotovsky]]])

* (14.03.24) Dimension-free concentration of sample covariance matrix in the spectral norm (Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]])

* (21.03.24) Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]], Concentration of Lipshitz and separately convex function of bounded random variables (Theorem 6.10 from [[#blm|[BLM]]]), Concentration of the supremum of an empirical process (Section 3.4 of [[#wainwright|[Wainwright]]])

=== Statistics ===

(10.04.24) Linear Regression. Bayesian information criterion. (Theorem 2.4 from [[#rigollet | [Rigollet]]])

(17.04.24) Restricted isometry property and epsilon-incoherence. (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]])

(24.04.24) Incoherence of a random matrix with independent Rademacher entries (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]]). Empirical risk minimization and Rademacher complexity (Sections 4.1-4.2 of [[#wainwright|[Wainwright]]]). Bounds on the Rademacher complexity of finite and finite-dimensional classes can be found in [[#paris | [Paris]]], Theorems 6.1 and 6.3.

(15.05.24) VC-dimension, Sauer's lemma. (Section 4.3 of [[#wainwright|[Wainwright]]]).

(22.05.24) Packing-covering duality. (Lemma 5.12 of [[#van_handel|[van Handel]]]). Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

(29.05.24) Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

(05.06.24) Offset Rademacher Complexity (some parts of [[#puchkin|[Puchkin]]]

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Zhivotovsky] [https://disk.yandex.ru/i/3yAKKiHc73ZSZQ Nikita Zhivotovskiy. Dimension-free bounds for sums of independent matrices and simple tensors via the variational principle. Electron. J. Probab. vol. 29 (2024), article no. 13, 1–28.]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

[Puchkin] [https://proceedings.mlr.press/v195/puchkin23a.html Nikita Puchkin, Nikita Zhivotovskiy. Exploring Local Norms in Exp-concave Statistical Learning. COLT 2023]

High-dimensional Probability and Statistics

2024-06-06T13:51:31Z

Fedor.Noskov:

= Classes =

Wednesdays 16:20–17:40, in room R307.

Teachers: [https://www.hse.ru/en/staff/anaumov Alexey Naumov], [https://www.hse.ru/en/org/persons/133709471 Quentin Paris]

Teaching Assistant: [https://t.me/teddy_nos Fedor Noskov]

= Lecture content =

* (17.01.24) Chapter 1 from [[#van_handel|[van Handel]]]

= Seminar content =

=== Probability ===

* (17.01.24) Example 2.4 from [[#wainwright|[Wainwright]]], Lemma 2.2 from [[#blm|[BLM]]]

* (24.01.24) Appendix A and Exercise 2.2 of the second chapter of [[#wainwright|[Wainwright]]], Section 2.5.1 from [[#vershynin|[Vershynin]]]

* (31.01.24) Section 2.1.3 and Example 2.12 from [[#wainwright|[Wainwright]]]

* (07.02.24) Section 2.3 from [[#wainwright|[Wainwright]]]

* (24.02.24) Herbst's argument (Proposition 3.2 from [[#wainwright|[Wainwright]]]), Sub-additivity of the entropy (Theorem 4.22 from [[#blm|[BLM]]]), logorithmic Sobolev inequality for Gaussian random variables (Theorem 5.5 from [[#blm|[BLM]]])

* (07.03.24) PAC-Bayesian inequality. (Lemma 2.1 from [[#zhivotovsky | [Zhivotovsky]]])

* (14.03.24) Dimension-free concentration of sample covariance matrix in the spectral norm (Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]])

* (21.03.24) Theorem 1.2 of [[#zhivotovsky | [Zhivotovsky]]], Concentration of Lipshitz and separately convex function of bounded random variables (Theorem 6.10 from [[#blm|[BLM]]]), Concentration of the supremum of an empirical process (Section 3.4 of [[#wainwright|[Wainwright]]])

=== Statistics ===

(10.04.24) Linear Regression. Bayesian information criterion. (Theorem 2.4 from [[#rigollet | [Rigollet]]])

(17.04.24) Restricted isometry property and epsilon-incoherence. (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]])

(24.04.24) Incoherence of a random matrix with independent Rademacher entries (Incoherence section, pp.59-62 of [[#rigollet | [Rigollet]]]). Empirical risk minimization and Rademacher complexity (Sections 4.1-4.2 of [[#wainwright|[Wainwright]]]). Bounds on the Rademacher complexity of finite and finite-dimensional classes can be found in [[#paris | [Paris]]], Theorems 6.1 and 6.3.

(15.05.24) VC-dimension, Sauer's lemma. (Section 4.3 of [[#wainwright|[Wainwright]]]).

(22.05.24) Packing-covering duality. (Lemma 5.12 of [[#van_handel|[van Handel]]]). Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

(29.05.24) Uniform bound on the metric entropy via VC-dimension (Theorem 7.16 of [[#van_handel|[van Handel]]])

(05.06.24) Offset Rademacher Complexity (some parts of [[#puchkin|[Puchkin]]]

= References =

links are available via hse accounts

[van Handel] [https://disk.yandex.ru/i/GPfpvZt7lSf7Bg Ramon van Handel. Probability in High Dimensions, Lecture Notes]

[Vershynin] [https://disk.yandex.ru/i/ci_c_FW5-eXH-A R. Vershynin. High-Dimensional Probability]

[Wainwright] [https://disk.yandex.ru/i/4RUmUDj3--sNOQ M.J. Wainwright. High-Dimensional Statistics]

[BLM] [https://disk.yandex.ru/i/7GOknoh1HYGEJQ Boucheron et al. Concentration inequalities]

[Zhivotovsky] [https://disk.yandex.ru/i/3yAKKiHc73ZSZQ Nikita Zhivotovskiy. Dimension-free bounds for sums of independent matrices and simple tensors via the variational principle. Electron. J. Probab. vol. 29 (2024), article no. 13, 1–28.]

[Puchkin] [https://proceedings.mlr.press/v195/puchkin23a.html Nikita Puchkin, Nikita Zhivotovskiy. Exploring Local Norms in Exp-concave Statistical Learning. COLT 2023]

[Rigollet] [https://disk.yandex.ru/i/GW7kFWmdsrfClA Philippe Rigollet and Jan-Christian H¨utter. High-Dimensional Statistics. Lecture Notes]

[Paris] [https://disk.yandex.ru/i/CeIEx0MW2hpbrw Quentin Paris. Statistical Learning Theory. Lecture Notes]

High-dimensional Probability and Statistics

2024-06-06T13:12:24Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-06-06T13:09:03Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-06-06T13:05:56Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-03-21T12:26:08Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-01-29T09:51:14Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-01-29T09:50:33Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-01-24T15:28:43Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-01-24T15:28:13Z

Fedor.Noskov:

High-dimensional Probability and Statistics

2024-01-24T15:27:25Z

Fedor.Noskov: protection notification

High-dimensional Probability and Statistics

2024-01-23T13:58:15Z

Fedor.Noskov: HDPS 2024

Математическая статистика 2023/24 (пилотный поток)

2024-01-22T13:28:00Z

Fedor.Noskov: Добавил классрум 223

== Преподаватели и учебные ассистенты ==
{| class="wikitable" style="text-align:center"
|-
! Группа !! БПМИ221 !! БПМИ222 !! БПМИ223 !! БПМИ224
|-
|| Лектор ||colspan="4"| [https://www.hse.ru/org/persons/218368371 Пучкин Никита Андреевич]
|-
|| Семинарист || [https://t.me/ssamsonov Сергей Самсонов] || [https://t.me/dashademidova Дарья Демидова] || [https://t.me/teddy_nos Федор Носков] || [https://t.me/nikita_puchkin Никита Пучкин]
|-
|| Ассистент(ы) || [https://t.me/ninehundredninetythree Юрий Пустовалов] [https://t.me/sudakovcom Илья Судаков] || [https://t.me/spankevich Сергей Панкевич] [https://t.me/a_ladyseva Алина Ладысева] || [https://t.me/w8ing Всеволод Куйбида] [https://t.me/volyachka Ольга Турчина] || [https://t.me/MarynaHorbach Марина Горбач] [https://t.me/guywithlowstandarts Егор Чернявский]
|-
|| Группа в Telegram || [https://t.me/+I8eFbxEoHyQwMzIy Группа 221] || [https://t.me/+QaGwbt_nR9w0NTc6 Группа 222] || [https://t.me/+ZW93ZzIBCuoxMjBi Группа 223] || [https://t.me/+_mHMMcZ6nO00YzAy Группа 224]
|-
|colspan="5"| [https://t.me/+x0NabvfxeaowZGJi Чат пилотного потока в Telegram]
|}

== Организационные моменты ==
=== Правила игры ===
Оценка за курс вычисляется по формуле

Оитог = 0.2 * ДЗ + 0.15 * K1 + 0.25 * КР + 0.15 * K2 + 0.25 * Э,

где

* ДЗ — оценка за домашние задания;
* K1 — оценка за первый коллоквиум;
* КР — оценка за контрольную работу;
* K2 — оценка за второй коллоквиум;
* Э — оценка за экзамен.

Округляется только итоговый балл. Правило округления стандартное (арифметическое).

=== Ведомость с оценками ===

{| class="wikitable" style="text-align:center"
|-
! [ 221] !! [ 222] !! [223] !! [ 224]
|}

=== Ссылки на классрум ===

{| class="wikitable" style="text-align:center"
|-
! [ 221] !! [ 222] !! [https://classroom.google.com/c/NjU0NTIxNzE4NjM3?cjc=jyoujy3 223] !! [ 224]
|}

== Материалы ==

[https://disk.yandex.ru/d/zl7DgU7FmuJKLg/%D0%9C%D0%B0%D1%82%D0%B5%D0%BC%D0%B0%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B0%D1%8F%20%D1%81%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D0%BA%D0%B0%20(%D0%BF%D0%B8%D0%BB%D0%BE%D1%82%D0%BD%D1%8B%D0%B9%20%D0%BF%D0%BE%D1%82%D0%BE%D0%BA) '''Видеозаписи лекций''']

[https://disk.yandex.ru/i/F5nTjQH9AFZqXQ '''Обновляемый конспект лекций''']

=== Материалы семинаров: ===
{| class="wikitable" style="text-align:center"
|-
! БПМИ221 !! БПМИ222 !! БПМИ223 !! [https://disk.yandex.ru/d/cdJQc_Wmin2cjA БПМИ224]
|}

== Домашние задания ==
=== Список ДЗ ===
{| class="wikitable" style="text-align:center"
|-
! [https://disk.yandex.ru/i/sxczUbKAi4ttKQ Домашнее задание №1, дедлайн - 24.01, 23:59]
|}

== Список рекомендуемой литературы ==
=== Учебники ===
* Ивченко Г. И., Медведев Ю. И. [https://disk.yandex.ru/i/waXgDQWDh_rgTA Введение в математическую статистику ]

* Лагутин М. Б. [http://iosipoi.com/teachingfiles/stat/Lagutin.pdf Наглядная математическая статистика]

* Бородин А. Н. [https://disk.yandex.ru/i/Ubk5YLMk_PJjYw Элементарный курс теории вероятностей и математической статистики]

* Боровков А. А. [https://disk.yandex.ru/i/212K-4gWWwjQzA Математическая статистика]

* Wasserman L. A. [https://egrcc.github.io/docs/math/all-of-statistics.pdf All of Statistics: A Concise Course in Statistical Inference]

=== Задачники ===
* Коршунов Д. А., Чернова Н. И. [https://disk.yandex.ru/i/TB9dxbQ1Nz1JyA Сборник задач и упражнений по математической статистике]

== Страницы прошлых лет ==

[http://wiki.cs.hse.ru/Математическая_статистика_2022/2023_(пилотный_поток) 2022/2023 учебный год]

Математическая статистика 2023/24 (пилотный поток)

2024-01-09T09:05:29Z

Fedor.Noskov: добавил группу 223

== Преподаватели и учебные ассистенты ==
{| class="wikitable" style="text-align:center"
|-
! Группа !! БПМИ221 !! БПМИ222 !! БПМИ223 !! БПМИ224
|-
|| Лектор ||colspan="4"| [https://www.hse.ru/org/persons/218368371 Пучкин Никита Андреевич]
|-
|| Семинарист || [https://t.me/ssamsonov Сергей Самсонов] || [https://t.me/dashademidova Дарья Демидова] || [https://t.me/teddy_nos Федор Носков] || [https://t.me/nikita_puchkin Никита Пучкин]
|-
|| Ассистент(ы) || [https://t.me/ninehundredninetythree Юрий Пустовалов] [https://t.me/sudakovcom Илья Судаков] || [https://t.me/spankevich Сергей Панкевич] [https://t.me/a_ladyseva Алина Ладысева] || [https://t.me/w8ing Всеволод Куйбида] [https://t.me/volyachka Ольга Турчина] || [https://t.me/MarynaHorbach Марина Горбач] [https://t.me/guywithlowstandarts Егор Чернявский]
|-
|| Группа в телеграмме || [https://t.me/+I8eFbxEoHyQwMzIy Группа 221] || [Группа 222] || [https://t.me/+ZW93ZzIBCuoxMjBi Группа 223] || [https://t.me/+_mHMMcZ6nO00YzAy Группа 224]
|}

== Организационные моменты ==
=== Правила игры ===
Оценка за курс вычисляется по формуле

Оитог = 0.2 * ДЗ + 0.15 * K1 + 0.25 * КР + 0.15 * K2 + 0.25 * Э,

где

* ДЗ — оценка за домашние задания, вычисляемая как отношение суммы набранных баллов за решения задач к максимальному количеству баллов, которое можно было набрать за решение всех задач из домашних заданий;
* K1 — оценка за первый коллоквиум;
* КР — оценка за контрольную работу;
* K2 — оценка за второй коллоквиум;
* Э — оценка за экзамен.

Округляется только итоговый балл. Правило округления стандартное (арифметическое).

=== Ведомость с оценками ===

{| class="wikitable" style="text-align:center"
|-
! [ 221] !! [ 222] !! [ 223] !! [ 224]
|}

=== Ссылки на классрум ===

{| class="wikitable" style="text-align:center"
|-
! [ 221] !! [ 222] !! [ 223] !! [ 224]
|}

== Материалы ==

[https://disk.yandex.ru/i/F5nTjQH9AFZqXQ '''Обновляемый конспект лекций (пилотный поток)''']

=== Материалы семинаров: ===
{| class="wikitable" style="text-align:center"
|-
! БПМИ221 !! БПМИ222 !! БПМИ223 !! [https://disk.yandex.ru/d/cdJQc_Wmin2cjA БПМИ224]
|}

== Домашние задания ==
=== Список ДЗ ===
{| class="wikitable" style="text-align:center"
|-
! БПМИ221 !! БПМИ222 !! БПМИ223 !! БПМИ224
|}

== Список рекомендуемой литературы ==
=== Учебники ===
* Ивченко Г. И., Медведев Ю. И. [https://disk.yandex.ru/i/waXgDQWDh_rgTA Введение в математическую статистику ]

* Лагутин М. Б. [http://iosipoi.com/teachingfiles/stat/Lagutin.pdf Наглядная математическая статистика]

* Бородин А. Н. [https://disk.yandex.ru/i/Ubk5YLMk_PJjYw Элементарный курс теории вероятностей и математической статистики]

* Боровков А. А. [https://disk.yandex.ru/i/212K-4gWWwjQzA Математическая статистика]

* Wasserman L. A. [https://egrcc.github.io/docs/math/all-of-statistics.pdf All of Statistics: A Concise Course in Statistical Inference]

=== Задачники ===
* Коршунов Д. А., Чернова Н. И. [https://disk.yandex.ru/i/TB9dxbQ1Nz1JyA Сборник задач и упражнений по математической статистике]

== Страницы прошлых лет ==

[http://wiki.cs.hse.ru/Математическая_статистика_2022/2023_(пилотный_поток) 2022/2023 учебный год]

Математическая статистика 2022/2023 (пилотный поток)

2023-01-19T13:07:16Z

Fedor.Noskov: Добавил classroom 214

== Преподаватели и учебные ассистенты ==
{| class="wikitable" style="text-align:center"
|-
! Группа !! БПМИ211 !! БПМИ212 !! БПМИ214
|-
|| Лектор ||colspan="3"| [https://www.hse.ru/staff/anaumov Наумов Алексей Александрович]
|-
|| Семинарист || [https://t.me/unkoll Даниил Тяпкин] || [https://t.me/nikita_puchkin Никита Пучкин] || [https://t.me/ssamsonov Сергей Самсонов]
|-
|| Ассистент(ы) || [https://t.me/DimaLishudi Дмитрий Лишуди] [https://t.me/sheshamar Шешукова Марина] || [https://t.me/Secret_pirogok Олег Курилов] [https://t.me/a_ladyseva Алина Ладысева] || [https://t.me/teddy_nos Федор Носков]
|-
|| Группа в телеграмме || [https://t.me/+0G5SmwgtfK5jN2Q6 Группа 211] || [https://t.me/+gEgqWXV0qKtiYWU6 Группа 212] || [https://t.me/+_O3XnWegUSQ3OWE6 Группа 214]
|}

== Организационные моменты ==
=== Правила игры ===
Оценка за курс складывается из нескольких факторов:
* Одна контрольная работа (письменная, ориентировочно после 3-го модуля);
* Два коллоквиума;
* Домашние задания. В среднем, каждую неделю будут выдавать по 2-3 задачи для самостоятельного решения, которые будет нужно письменно сдавать ассистентам;
* Письменный экзамен;

Округляется только итоговый балл. Правило округления стандартное (арифметическое).
* Оценка высчитывается по следующей формуле:

Оитог = 0.2 * ОКР + 0.15 * Околлоквиум 1 + 0.15 * Околлоквиум 2 + 0.2 * ОДЗ + 0.3 * Оэкзамен.

Без уважительной причины студент может сдать 1 ДЗ за семестр после дедлайна (оно оценивается без штрафа). В случае наличия уважительной причины ситуация решается индивидуально.

=== Ведомость с оценками ===

{| class="wikitable" style="text-align:center"
|-
! [https://docs.google.com/spreadsheets/d/1w_AQSzEIQWHS3z-SJ64WDcBYHW6CCOEocBhztRiQyCU/edit#gid=1963363551 211] !! [https://docs.google.com/spreadsheets/d/1w_AQSzEIQWHS3z-SJ64WDcBYHW6CCOEocBhztRiQyCU/edit#gid=1691258399 212] !!
[https://docs.google.com/spreadsheets/d/1w_AQSzEIQWHS3z-SJ64WDcBYHW6CCOEocBhztRiQyCU/edit#gid=417749367 214]
|}

=== Ссылки на классрум ===

{| class="wikitable" style="text-align:center"
|-
! [https://classroom.google.com/c/NTg0MTYwMjU0NTEz?cjc=4yve7s3 211] !! [https://classroom.google.com/c/NTgzNTY4NDAwNDkz?cjc=ctbi66m 212] !!
[https://classroom.google.com/c/NTg0NDcxNTcyMzIz?cjc=smj3vrl 214]
|}

== Контрольные работы ==

=== Правила игры ===

=== Сводка ===

== Коллоквиумы ==
=== Как проходят ===

=== Сводка ===

== Материалы ==
=== Лекции ===

'''Записи лекций:'''

[https://www.overleaf.com/read/dvjhrjzcgfsm Обновляемый конспект, пилотный поток]

'''Конспекты и видео семинаров:''' [https://www.youtube.com/@vvauijij6728/videos записи семинаров 212 группы]

=== Материалы: ===
{| class="wikitable" style="text-align:center"
|-
! БПМИ211 !! БПМИ212 !! БПМИ214
|-
! [10.01] !! [https://disk.yandex.ru/i/HOsIGbIO7z0a0g 09.01] !!
[10.01]
|-
! [17.01] !! [https://disk.yandex.ru/i/yFFgr-t7gIFqyA 16.01] !! [https://disk.yandex.ru/i/r0nvAROEn8j4sA 17.01]
|}

== Домашние задания ==
=== Список ДЗ ===
{| class="wikitable" style="text-align:center"
|-
! БПМИ211 !! БПМИ212 !! БПМИ214
|-
! [https://disk.yandex.ru/i/LlWrihxtuodR7g Домашнее задание №1, дедлайн - 27.01, 23:59] !! [https://disk.yandex.ru/i/6g-hhZmR22UkAg Домашнее задание №1, дедлайн - 23.01, 23:59] !!
[https://disk.yandex.ru/i/5uCbZYnfRPgy6A Домашнее задание №1, дедлайн - 24.01, 23:59]
|-
! !! [https://disk.yandex.ru/i/jHgE1oIWyXR7bQ Домашнее задание №2, дедлайн - 30.01, 23:59] !!
|}

== Список рекомендуемой литературы ==
=== Учебники ===
* Ивченко Г. И., Медведев Ю. И. [https://disk.yandex.ru/i/waXgDQWDh_rgTA Введение в математическую статистику ]

* Лагутин М. Б. [http://iosipoi.com/teachingfiles/stat/Lagutin.pdf Наглядная математическая статистика]

* Бородин А. Н. [https://disk.yandex.ru/i/Ubk5YLMk_PJjYw Элементарный курс теории вероятностей и математической статистики]

* Боровков А. А. [https://disk.yandex.ru/i/212K-4gWWwjQzA Математическая статистика]

* Wasserman L. A. [https://egrcc.github.io/docs/math/all-of-statistics.pdf All of Statistics: A Concise Course in Statistical Inference]

=== Задачники ===
* Коршунов Д. А., Чернова Н. И. [https://disk.yandex.ru/i/TB9dxbQ1Nz1JyA Сборник задач и упражнений по математической статистике]

== Страницы прошлых лет ==

'''Предупреждение:''' программа курса значительно изменилась по сравнению с прошлыми годами.

[http://wiki.cs.hse.ru/Теория_вероятностей_и_математическая_статистика_2020/2021_(пилотный_поток) 2020/2021 учебный год]

[http://wiki.cs.hse.ru/Теория_вероятностей_и_математическая_статистика_2019/2020_(пилотный_поток) 2019/2020 учебный год]