Modern Data Analysis 2021 2022 — различия между версиями

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(Homeworks)
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* Homework 1: Classification
 
* Homework 1: Classification
  
Announced: 11.10.2021
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'''Announced''': 11.10.2021
  
Soft deadline: 03.11.2021
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'''Soft deadline''': 03.11.2021
  
Hard deadline: 10.11.2021
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'''Hard deadline''': 10.11.2021
  
=== Lecture 1===
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=== Lectures ===
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== 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===
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== 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===
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== 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===
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== Seminar 1==
  
 
Classification  
 
Classification  
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=== Lecture 4===
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== 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 13:05, 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.