Introduction to Machine Learning and Data Mining 2020 — различия между версиями

Версия 12:45, 6 февраля 2020

Lecturer: Dmitry Ignatov

TA: Dmitry Egurnov

Homeworks

Homework 1: To be announced

Lecture on 16 Jan 2020

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

Practice: demonstration with Orange.

Lecture on 23 Jan 2020

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

Practice: demonstration with Orange.

Lecture on 30 Jan 2020

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

Practice: demonstration with Orange and web-demo.

Practice on 6 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.