Data analysis (Software Engineering) - История изменений

Sarfaraz1235: course reference added.

2021-08-27T07:25:30Z

course reference added.

← Предыдущая версия		Версия от 07:25, 27 августа 2021
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	In this class we consider the main problems of data mining and machine learning: classification, clustering, regression, dimensionality reduction, ranking, collaborative filtering. We will also study mathematical methods and concepts which data analysis is based on as well as formal assumptions behind them and various aspects of their implementation.		In this class we consider the main problems of data mining and machine learning: classification, clustering, regression, dimensionality reduction, ranking, collaborative filtering. We will also study mathematical methods and concepts which data analysis is based on as well as formal assumptions behind them and various aspects of their implementation.

	A significant attention is given to practical skills of data analysis that will be developed on seminars by studying the Python programming language and relevant libraries for scientific computing.		A significant attention is given to practical skills of data analysis that will be developed on seminars by studying the Python programming language and relevant libraries for scientific computing. To learn more join [https://www.edureka.co/python-programming-certification-training Python training] today.

	The knowledge of linear algebra, real analysis and probability theory is required.		The knowledge of linear algebra, real analysis and probability theory is required.

Dganenko в 11:28, 14 октября 2019

2019-10-14T11:28:51Z

← Предыдущая версия		Версия от 11:28, 14 октября 2019
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	* [http://python.org Official website]		* [http://python.org Official website]
	* Libraries: [http://www.numpy.org/ NumPy], [http://pandas.pydata.org/ Pandas], [http://scikit-learn.org/stable/ SciKit-Learn], [http://matplotlib.org/ Matplotlib].		* Libraries: [http://www.numpy.org/ NumPy], [http://pandas.pydata.org/ Pandas], [http://scikit-learn.org/stable/ SciKit-Learn], [http://matplotlib.org/ Matplotlib].
	* A little example for the ~~begginers~~: [http://nbviewer.ipython.org/gist/voron13e02/83a86f2e0fc5e7f8424d краткое руководство с примерами по Python 2]		* A little example for the beginners: [http://nbviewer.ipython.org/gist/voron13e02/83a86f2e0fc5e7f8424d краткое руководство с примерами по Python 2]
	* Python from scratch: [http://nbviewer.ipython.org/gist/rpmuller/5920182 A Crash Course in Python for Scientists]		* Python from scratch: [http://nbviewer.ipython.org/gist/rpmuller/5920182 A Crash Course in Python for Scientists]
	* Lectures [https://github.com/jrjohansson/scientific-python-lectures#online-read-only-versions Scientific Python]		* Lectures [https://github.com/jrjohansson/scientific-python-lectures#online-read-only-versions Scientific Python]

Dganenko: Тест

2019-10-09T15:22:54Z

Тест

← Предыдущая версия		Версия от 15:22, 9 октября 2019
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	== Useful links ==		== Useful links ==
	[[Тест\|Т]]		[[Тест\|.]]
	=== Machine learning ===		=== Machine learning ===
	* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]		* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]

Dganenko: Тест

2019-10-09T15:21:52Z

Тест

← Предыдущая версия		Версия от 15:21, 9 октября 2019
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	== Useful links ==		== Useful links ==
	[[Т]]		[[Тест\|Т]]
	=== Machine learning ===		=== Machine learning ===
	* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]		* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]

Dganenko: /* Useful links */

2019-10-09T15:18:33Z

Useful links

← Предыдущая версия		Версия от 15:18, 9 октября 2019
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	== Useful links ==		== Useful links ==
	[[~~Тест~~]]		[[Т]]
	=== Machine learning ===		=== Machine learning ===
	* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]		* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]

Dmashkovcev: Тест

2019-10-09T14:26:48Z

Тест

← Предыдущая версия		Версия от 14:26, 9 октября 2019
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	== Useful links ==		== Useful links ==
	[[DJ]]		[[Тест]]
	=== Machine learning ===		=== Machine learning ===
	* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]		* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]

Dmashkovcev: Временная ссылка на тестовую страницу

2019-10-09T14:24:39Z

Временная ссылка на тестовую страницу

← Предыдущая версия		Версия от 14:24, 9 октября 2019
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	== Useful links ==		== Useful links ==
			[[DJ]]
	=== Machine learning ===		=== Machine learning ===
	* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]		* [http://www.machinelearning.ru/wiki/index.php?title=Заглавная_страница machinelearning.ru]

Tipt0p: Отмена правки 22303, сделанной участником Apogentus (обс.)

2017-01-13T20:38:08Z

Отмена правки 22303, сделанной участником Apogentus (обс.)

← Предыдущая версия		Версия от 20:38, 13 января 2017
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			'''This page is for 2016 year!'''

	'''Scores and deadlines: [~~---~~ here]'''		'''Scores and deadlines: [https://drive.google.com/open?id=1TQ97B8rqC7sUxTnCMKXoskgRPXO8rAyWoBWBezY58h4 here]'''

	'''Class email:''' cshse.ml@gmail.com		'''Class email:''' cshse.ml@gmail.com

	'''Anonymous feedback form:''' [~~https~~://~~docs.google~~.~~com~~/forms/~~d/12E9Bsd8UpY9KpEdhOsKdPOawcTY1g-7mpefcSh7Qm7Y~~ here]		'''[https://docs.google.com/forms/d/100_gMWQwp41zpHgKuf3fl3SpFSwNj6ggL13DtnxWEEw/viewform Anonymous overall course evaluation form] '''

			'''Anonymous feedback form:''' [http://goo.gl/forms/CT3h4QaMeB here]
	<br />		<br />

	== Announcements ==		== Announcements ==

			===Kaggle evaluation===
			Kaggle evaluation [https://docs.google.com/spreadsheets/d/1h90zLpQ2Q8QD_xQLGJLY1xljnLGF175Re4fTMChHFgA/edit?usp=sharing is available here]. Please check that your work is in the list. Presentations were evaluated using [https://inclass.kaggle.com/c/hse-spring2016-stack-overflow/rules the rules of the competition]. In particular - I was expecting to see:
			* that you tried different methods
			* table with accuracy results of each method
			* description how you tuned the parameters of your model (over which grid, with graphs showing accuracy change)
			* data analysis and insights described with illustrative visualizations.
			* in feature selection: quantitive results how each feature could be helpful/not helpful.

			===Exam questions===
			Exam questions are published and available [https://yadi.sk/i/Gi_T_R4AsD2aU here].

			===Kaggle presentation requirements===
			You should send presentations before June 3 (Friday) 23-59. Presentations should be sent to v.v.kitov@yandex.ru. The title should be "HSE kaggle presentation <team name>". On the title page of the presentation you should list all team participants. Presentation should be in pdf or ppt format and have all components listed in [https://inclass.kaggle.com/c/hse-spring2016-stack-overflow/rules competition rules]. Code in py or ipynb format should also be attached to the letter (it may consist of several files).

			===Early exam===
			On June 6th, 13-40 - 16-30 there will two lessons. They will cover:
			1) a consultation before exam. Please read through all the material and come with your questions.
			2) Presentations of top-3 kaggle teams with their solutions (15 minutes each). Teams with over 60 submissions are welcome to tell their findings in the data - what worked and what not (10 minutes each). Everybody else is also welcome (not obliged) to participate with short presentations (5-10 minutes) and tell interesting findings in the data and non-standard approaches that you tried (not necessarily successful).

			For your convenience there will be a possibility to take exam in data analysis earlier - on June 6th at 16-40. To take exam earlier you need to request participation to e-mail v.v.kitov@yandex.ru. The number of participants is limited. Note that earlier exam will be the same as official exam and they mutually exclude each other, so you need to select in which exam to participate. Exam program will be available soon. Earlier exam schedule is proposed for your convenience - to give you the possibility to fully concentrate on preparation to data analysis exam.

	== Course description ==		== Course description ==
	In this class we consider the main problems of machine learning ~~and data analysis~~: classification, regression, dimensionality reduction, ~~clustering~~, collaborative filtering. We will also study mathematical methods and concepts which ~~machine learning~~ is based on as well as formal assumptions behind them and various aspects of their implementation.		In this class we consider the main problems of data mining and machine learning: classification, clustering, regression, dimensionality reduction, ranking, collaborative filtering. We will also study mathematical methods and concepts which data analysis is based on as well as formal assumptions behind them and various aspects of their implementation.

	A significant attention is given to practical skills of data analysis that will be developed on seminars by studying the Python programming language and relevant libraries for scientific computing.		A significant attention is given to practical skills of data analysis that will be developed on seminars by studying the Python programming language and relevant libraries for scientific computing.

	~~==Prerequisites==~~		The knowledge of linear algebra, real analysis and probability theory is required.
	~~Firm~~ knowledge of linear algebra, ~~mathematical~~ analysis and probability theory is required.

	'''The class consists of:'''		'''The class consists of:'''
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	# Midterm theoretical colloquium		# Midterm theoretical colloquium
	# Final exam		# Final exam

			== Events outside the course ==

			[https://it.mail.ru/announcements/36/?utm_campaign=newsletter&utm_medium=email&utm_source=newsletter_2742016df Universal recomendation system of mail.ru]

			[https://www.youtube.com/channel/UCeq6ZIlvC9SVsfhfKnSvM9w Description of solutions to different competitions on Kaggle]

			[http://ria.ru/science/20160514/1432666353.html Neural networks adapt videos to the painting style of famous artists.]

	== Syllabus ==		== Syllabus ==

Tipt0p: Отмена правки 22307, сделанной участником Apogentus (обс.)

2017-01-13T20:37:53Z

Отмена правки 22307, сделанной участником Apogentus (обс.)

← Предыдущая версия		Версия от 20:37, 13 января 2017
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	== Syllabus ==		== Syllabus ==

	# Introduction~~. Core concepts of~~ machine learning.		# Introduction to machine learning.
	# K-nearest neighbours ~~method~~.		# K-nearest neighbours classification and regression. Extensions. Optimization techniques.
	# Decision tree methods.		# Decision tree methods.
	# ~~Regression methods. Regularization~~.		# Bayesian decision theory. Model evaluation:
	# ~~Convex functions. Classification with linear~~ methods. ~~Loss functions.~~		# Linear classification methods. Adding regularization to linear methods.
	~~# Classification with~~ linear methods~~. Gradient descent and stochastic gradient descent. Regularization~~.		# Regression.
	# ~~Model evaluation. Logistic regression~~.		# Kernel generalization of standard methods.
	# ~~Support vector machines~~.		# Neural networks.
	# ~~Generalization with kernels~~.		# Ensemble methods: bagging, boosting, etc.
	# ~~Linear dimensionality reduction - PCA~~, ~~SVD~~.
	# Feature selection.		# Feature selection.
	# ~~Ensemble methods.~~		# Feature extraction
	# ~~Boosting~~. ~~xgBoost.~~		# EM algorithm. Density estimation using mixtures.
	~~# Neural networks - architecture.~~		# Clustering
	~~# Neural networks - optimization~~.
	# Clustering~~. EM algorithm for Gaussian mixtures.~~
	# Collaborative filtering		# Collaborative filtering
	# ~~Bayes decision theory. Naive Bayes assumption. Kernel density estimation.~~		# Ranking
	~~# Semi-supervised learning~~
	~~# Active learnning~~

	== Lecture materials ==		== Lecture materials ==

Tipt0p: Отмена правки 22308, сделанной участником Apogentus (обс.)

2017-01-13T20:37:43Z

Отмена правки 22308, сделанной участником Apogentus (обс.)

← Предыдущая версия		Версия от 20:37, 13 января 2017
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	[https://yadi.sk/i/RajIebEkmqgzw Download]		[https://yadi.sk/i/RajIebEkmqgzw Download]

			Additional materials: [https://yadi.sk/i/x2lrKdbVmr2bf The Field Guide to Data Science], [http://www.machinelearning.ru/wiki/images/f/fc/Voron-ML-Intro-slides.pdf Лекция К.В.Воронцова]

			'''Lecture 2. K nearest neighbours method. '''

			[https://yadi.sk/i/Od8HM9h-nUWob Download]

			Additional materials: [http://www.machinelearning.ru/wiki/images/c/c3/Voron-ML-Metric-slides.pdf Лекция К.В.Воронцова], [http://arxiv.org/pdf/1306.6709v4.pdf Metric learning survey]

			'''Lecture 3. Decision trees. '''

			[https://yadi.sk/i/-vPl2vaBqXrt5 Download]

			Additional materials: ''Webb, Copsey "Statistical Pattern Recognition", chapter 7.2.''

			'''Lecture 4a. Model evaluation. '''

			Binary quality measures. ROC curve, AUC.

			[https://yadi.sk/i/7V7U_1QtnfYZQ Download]

			Additional materials: ''Webb, Copsey "Statistical Pattern Recognition", chapter 9.''

			'''Lecture 4b. Bayes minimum cost classification. '''

			Case of general losses, common within-class losses and 0,1 losses. Gaussian classifier.

			[https://yadi.sk/i/6jIvLiYMosuz5 Download]

			'''Lecture 5. Linear classifiers. '''

			Discriminant function. Invariance to monotonous transformations for them. Definition for multi-class and binary class cases.

			[https://yadi.sk/i/0IQ6P3LDoqpRk Download]

			Additional materials: [http://www.machinelearning.ru/wiki/images/5/53/Voron-ML-Lin-SG.pdf Лекции К.В.Воронцова по линейным методам классификации]

			'''Lecture 6. Support vector machines. '''

			Linear separable and linearly non-separable case. Equivalent definition with loss function. Support vectors and non-informative vectors.

			[https://yadi.sk/i/baP7gbWXoqpTE Download]

			'''Lecture 7. Kernel trick. '''

			Application of kernel trick to SVM. Gaussian, polynomial kernels.

			[https://yadi.sk/i/3W6A9FmZoqpU9 Download]

			'''Lecture 8. Regression. '''

			Linear regression and extensions: weighted regression, robust regression, different loss-functions, regression with non-linear features, locally-constant (Nadaraya-Watson) regression.

			[https://yadi.sk/i/HSp51pmepjQBq Download]

			'''Lecture 9. Boosting. '''

			Forward stagewise additive modelling. AdaBoost. Gradient boosting.

			[https://yadi.sk/i/3yTKLDcCpjLhG Download]

			Additional materials:

			[http://statweb.stanford.edu/~tibs/ElemStatLearn/ Friedman, Hastie, Tibshirani "The Elements of Statistical Learning"] - section 10: Boosting and additive trees.,

			[http://www.recognition.mccme.ru/pub/RecognitionLab.html/slbook.pdf Мерков "Введение в методы статистического обучения"] - секция 4: Линейные комбинации распознавателей.

			'''Lecture 10. Ensemble methods. '''

			Motivation. Bias-variance tradeoff. Bagging, RandomForest, ExtraRandomTrees. Stacking.

			[https://yadi.sk/i/8E-wZxIpq9Sgf Download]

			'''Lectures 11, 12. Summary. '''

			'''Lecture 13. Feature selection. '''

			[https://yadi.sk/i/2ZLC3J6dr3iAc Download]

			'''Lecture 14. Principal components analysis. '''

			[https://yadi.sk/i/6DxoScrKrN3LK Download]

			'''Lecture 14. Singular values decomposition. '''

			[https://yadi.sk/i/zwJftdkUrN6Td Download] - updated pages 17,18,19.

			'''Lecture 15. Working with text. '''

			[https://yadi.sk/i/YqdRr-0erEXba Download]

			'''Lecture 16. Neural networks. '''

			[https://yadi.sk/i/9yMM7mkrrEXbc Download]

			'''Lecture 17. Parametric distributions. '''

			[https://yadi.sk/i/D33hdVXTrkxDN Download]

			'''Lecture 18. Clustering. '''

			[https://yadi.sk/i/3DJY4Oo7rkxEW Download]

			'''Lecture 19. Mixture densities, EM-algorithm. ''' - updated.

			[https://yadi.sk/i/22wLOL2krkxGL Download]

			'''Lecture 20. Recommender systems. '''

			[https://yadi.sk/i/mMhaZlqLrvjph Download]

			'''Lecture 21. Kernel density estimation. '''

			[https://yadi.sk/i/zF7ZPyTCsAjFH Download]

	== Seminars ==		== Seminars ==