Modern Data Analysis 2021 2022 — различия между версиями
Machine (обсуждение | вклад) |
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=== Lectures === | === Lectures === | ||
| − | == Lecture 1== | + | ==== 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? | ||
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Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
| − | == Lecture 2== | + | ==== 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). | ||
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Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
| − | == Lecture 3== | + | ==== Lecture 3==== |
Classification (continued). Quality metrics. ROC curves. | Classification (continued). Quality metrics. ROC curves. | ||
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Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
| − | == Seminar 1== | + | ==== Seminar 1==== |
Classification | Classification | ||
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| − | == Lecture 4== | + | ==== 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 13:06, 20 октября 2021
Содержание
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.
