Modern Data Analysis 2021 2022: различия между версиями
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* Homework 1: Classification | * Homework 1: Classification | ||
Announced: 11.10.2021 | '''Announced''': 11.10.2021 | ||
Soft deadline: 03.11.2021 | '''Soft deadline''': 03.11.2021 | ||
Hard deadline: 10.11.2021 | '''Hard deadline''': 10.11.2021 | ||
=== Lecture 1 | |||
=== Lectures === | |||
== Lecture 1== | |||
Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch? | Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch? | ||
| Строка 26: | Строка 29: | ||
Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
== Lecture 2== | |||
Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy). | Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy). | ||
| Строка 32: | Строка 35: | ||
Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
== Lecture 3== | |||
Classification (continued). Quality metrics. ROC curves. | Classification (continued). Quality metrics. ROC curves. | ||
| Строка 38: | Строка 41: | ||
Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
== Seminar 1== | |||
Classification | Classification | ||
| Строка 45: | Строка 48: | ||
== Lecture 4== | |||
Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan | Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan | ||
Practice: DBScan Demo. | Practice: DBScan Demo. | ||
Версия от 10:05, 20 октября 2021
Course: Modern Data Analysis (2021–2022)
Lecturer: Dmitry Ignatov
TA: TBA
All the material are available via our telegram channel.
Final mark formula: FM = 0.8 Homeworks + 0.2 Exam (under voting)
Homeworks
- Homework 1: Classification
Announced: 11.10.2021
Soft deadline: 03.11.2021
Hard deadline: 10.11.2021
Lectures
Lecture 1
Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?
Practice: demonstration with Orange.
Lecture 2
Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).
Practice: demonstration with Orange.
Lecture 3
Classification (continued). Quality metrics. ROC curves.
Practice: demonstration with Orange.
Seminar 1
Classification
Practice: scikit-learn.
Lecture 4
Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan
Practice: DBScan Demo.