Into to DataMining and Machine Learning 2020 2021 — различия между версиями
Machine (обсуждение | вклад) (→Lecture on 09 February 2020) |
Machine (обсуждение | вклад) (→Lecture on 6 Feb 2020) |
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| Строка 38: | Строка 38: | ||
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). | Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). | ||
| − | === Lecture on | + | === 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 === | === Practice on 13 Feb 2020 === | ||
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 18:34, 11 июня 2021
Lecturer: Dmitry Ignatov
TA: Stefan Nikolić
Содержание
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.
