Into to DataMining and Machine Learning 2020 2021 — различия между версиями
Machine (обсуждение | вклад) (→Lecture on 6 Feb 2020) |
Machine (обсуждение | вклад) |
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| Строка 10: | Строка 10: | ||
* Homework 3: Recommender Systems | * Homework 3: Recommender Systems | ||
| − | === Lecture on 16 January | + | === Lecture on 16 January 2021=== |
Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch? | Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch? | ||
| Строка 16: | Строка 16: | ||
Practice: demonstration with Orange. | Practice: demonstration with Orange. | ||
| − | === Lecture on 26 January | + | === Lecture on 26 January 2021=== |
Classification (continued). Quality metrics. ROC curves. | Classification (continued). Quality metrics. ROC curves. | ||
| Строка 28: | Строка 28: | ||
Practice: DBScan Demo. | Practice: DBScan Demo. | ||
| − | === Lecture on 09 February | + | === Lecture on 09 February 2021=== |
* Introduction to Clustering (continued). Density-based techniques. DBScan and Mean-shift. | * Introduction to Clustering (continued). Density-based techniques. DBScan and Mean-shift. | ||
| Строка 34: | Строка 34: | ||
* 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. | ||
| − | === Practice on 16 Feb | + | === Practice on 16 Feb 2021 === |
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). | Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). | ||
| Строка 45: | Строка 45: | ||
Software: Orange, SPMF, Concept Explorer. | Software: Orange, SPMF, Concept Explorer. | ||
| − | === Practice on | + | === Practice on 16 March 2021 === |
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 18:42, 11 июня 2021
Lecturer: Dmitry Ignatov
TA: Stefan Nikolić
Содержание
[убрать]Homeworks
- Homework 1: Spectral Clustering
- Homework 2:
- Homework 3: Recommender Systems
Lecture on 16 January 2021
Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?
Practice: demonstration with Orange.
Lecture on 26 January 2021
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 2021
- 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 2021
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 16 March 2021
Clustering with scikit-learn (k-means, hierarchical clustering, DBScan, MeanShift, Spectral Clustering). Parameters tuning and results' evaluation. Continued.
