Introduction to Machine Learning and Data Mining 2020 — различия между версиями
Machine (обсуждение | вклад) (Новая страница: «'''Lecturer:''' Dmitry Ignatov '''TA:''' Dmitry Egurnov === Homeworks === Homework 1: To be announced === Lecture on 16 Jan 2020=== Intro slides. Course pla…») |
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=== Practice on 6 Feb 2020 === | === Practice on 6 Feb 2020 === | ||
| − | Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral | + | Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). |
=== Lecture on 6 Feb 2020 === | === Lecture on 6 Feb 2020 === | ||
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. | ||
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| + | === Practice on 13 Feb 2020 === | ||
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| + | Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). Parameters tuning and results' evaluation. Continued. | ||
Текущая версия на 08:59, 16 февраля 2020
Lecturer: Dmitry Ignatov
TA: Dmitry Egurnov
Содержание
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.
Practice on 13 Feb 2020
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). Parameters tuning and results' evaluation. Continued.
