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

Версия 18:28, 11 июня 2021

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 Jan 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 Feb 2020

Introduction to Clustering (continued). Density-based techniques. DBScan and Mean-shift.

Practice on 16 Feb 2020

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

Lecture on 6 Feb 2020

Graph and spectral clustering. Min-cuts and normalized cuts. Laplacian matrix. Fiedler vector. Applications.

Practice on 13 Feb 2020

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