Modern Data Analysis 2021 2022 — различия между версиями
Machine (обсуждение | вклад) (Новая страница: «== Course: Modern Data Analysis (2021–2022) == '''Lecturer:''' Dmitry Ignatov '''TA:''' TBA All the material are available via our telegram channel. '''Fin…») |
Machine (обсуждение | вклад) (→Homeworks) |
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| (не показаны 3 промежуточные версии этого же участника) | |||
| Строка 14: | Строка 14: | ||
* Homework 1: Classification | * Homework 1: Classification | ||
| + | '''Announced''': 11.10.2021 | ||
| − | === Lecture 1=== | + | '''Soft deadline''': changed to 08.11.2021 due to pandemic regulations (before it was 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? | Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch? | ||
| Строка 21: | Строка 28: | ||
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). | ||
| Строка 27: | Строка 34: | ||
Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
| − | === Lecture 3=== | + | ==== Lecture 3==== |
Classification (continued). Quality metrics. ROC curves. | Classification (continued). Quality metrics. ROC curves. | ||
| Строка 33: | Строка 40: | ||
Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
| − | === Seminar 1=== | + | ==== Seminar 1==== |
Classification | Classification | ||
| Строка 40: | Строка 47: | ||
| − | === 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. | ||
Текущая версия на 14:34, 1 ноября 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: changed to 08.11.2021 due to pandemic regulations (before it was 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.
