Into to DataMining and Machine Learning 2020 2021

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Lecturer: Dmitry Ignatov

TA: Stefan Nikolić


Homeworks

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

Lecture on 16 January 2020

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

Practice: demonstration with Orange.

Lecture on 26 January 2020

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 2020

  • 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 2020

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 13 Feb 2020

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