Into to DataMining and Machine Learning 2020 2021 — различия между версиями

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(Lecture on 6 Feb 2020)
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* Homework 3: Recommender Systems
 
* Homework 3: Recommender Systems
  
=== Lecture on 16 January 2020===
+
=== Lecture on 16 January 2021===
  
 
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 on 26 January 2020===
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=== Lecture on 26 January 2021===
  
 
Classification (continued). Quality metrics. ROC curves.  
 
Classification (continued). Quality metrics. ROC curves.  
Строка 28: Строка 28:
 
Practice: DBScan Demo.
 
Practice: DBScan Demo.
  
=== Lecture on 09 February 2020===
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=== Lecture on 09 February 2021===
  
 
* Introduction to Clustering (continued). Density-based techniques. DBScan and Mean-shift.
 
* Introduction to Clustering (continued). Density-based techniques. DBScan and Mean-shift.
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* Graph and spectral clustering. Min-cuts and normalized cuts. Laplacian matrix. Fiedler vector. Applications.
 
* Graph and spectral clustering. Min-cuts and normalized cuts. Laplacian matrix. Fiedler vector. Applications.
  
=== Practice on 16 Feb 2020 ===
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=== Practice on 16 Feb 2021 ===
  
 
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering).
 
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering).
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Software: Orange, SPMF, Concept Explorer.
 
Software: Orange, SPMF, Concept Explorer.
  
=== Practice on 13 Feb 2020 ===
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=== Practice on 16 March 2021 ===
  
 
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). Parameters tuning and results' evaluation. Continued.
 
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). Parameters tuning and results' evaluation. Continued.

Версия 18:42, 11 июня 2021

Lecturer: Dmitry Ignatov

TA: Stefan Nikolić


Homeworks

  • Homework 1: Spectral Clustering
  • Homework 2:
  • Homework 3: Recommender Systems

Lecture on 16 January 2021

Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?

Practice: demonstration with Orange.

Lecture on 26 January 2021

Classification (continued). Quality metrics. ROC curves.

Practice: demonstration with Orange.

Lecture on 2 February 2021

Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan

Practice: DBScan Demo.

Lecture on 09 February 2021

  • Introduction to Clustering (continued). Density-based techniques. DBScan and Mean-shift.
  • Graph and spectral clustering. Min-cuts and normalized cuts. Laplacian matrix. Fiedler vector. Applications.

Practice on 16 Feb 2021

Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering).

Lecture on 9 March 2021

Frequent Itemsets. Association Rules. Algorithms: Apriori, FP-growth. Interestingness measures. Closed and maximal itemsets. Applications: 1) Taxonomies of Website Visitors and 2) Web advertising.

Software: Orange, SPMF, Concept Explorer.

Practice on 16 March 2021

Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). Parameters tuning and results' evaluation. Continued.