Instructor:
Teaching Assistant:
Classes
| Day of Week | Hour | Room |
|---|---|---|
| Wednesday | 14:00 - 16:00 | Room C - Online |
| Thursday | 14:00 - 16:00 | Room C - Online |
| Friday | 09:00 - 11:00 | Room A1 - Online |
Office hours - Ricevimento: Anna Monreale: Wednesday: 11:00-13:00 online using Teams (Appointment by email) Francesca Naretto: Monday: 15:00-18:00 online using Teams (Appointment by email)
| Day | Topic | Learning material | References | Video Lectures | |
|---|---|---|---|---|---|
| 15.09 14:15‑16:00 | Lecture deleted | ||||
| 1. | 16.09 14:15‑16:00 | Overview. Introduction to KDD | 2021-1-overview.pdf1-intro-dm.pdf | Chap. 1 Kumar Book | Video 1 Video 2 |
| 2. | 17.09 09:00-10:45 | Data Understanding | Slides DU | Chap.2 Kumar Book and additioanl resource of Kumar Book:Exploring Data If you have the first ed. of KUMAR this is the Chap 3 | Video 1 Video 2 |
| 3. | 22.09 14:15-16:00 | Data Understanding + Data Preparation | 3-data_preparation.pdf | Chap. 2 Kumar Book | Video |
| 4. | 23.09 14:15-16:00 | Data Preparation + Data Similarities. | 4-data_similarity.pdf | Data Similarity is in Chap. 2 | |
| 5. | 24.09 09:00-10:45 | Introduction to Clustering. Center-based clustering: kmeans | 5-basic_cluster_analysis-intro.pdf 6.1-basic_cluster_analysis-kmeans.pdf | Clustering is in Chap. 7 | |
| 6. | 29.09 14:15-16:00 | Hierarchical clustering | 7.basic_cluster_analysis-hierarchical.pdf | Chap. 7 Kumar Book | |
| 7. | 30.09 14:15-16:00 | Density based clustering. Clustering validity. Lab. DU | 8.basic_cluster_analysis-dbscan-validity.pdf Notebook DU tips Another Notebook on DU | Chap. 7 Kumar Book | |
| 8. | 01.10 09:00-10:45 | Python Lab - Clustering | Notebook CLustering Tips | ||
| 9. | 06.10 14:15-16:00 | Center-based clustering: Bisecting K-means, Xmeans, EM | 6.2-basic_cluster_analysis-kmeans-variants.pdf | Chap. 7 Kumar Book, clusteringmixturemodels.pdf xmeans.pdf | |
| 10. | 07.10 14:15-16:00 | Classification Problem. Decision Trees | 9.chap3_basic_classification-2020.pdf | Chap. 3 Kumar Book | |
| 08.10 09:00-10:45 | Lecture canceled | ||||
| 11. | 13.10 14:15-16:00 | Decision Trees + Classifier Evaluation | same slides of the previous lecture | Chap. 3 Kumar Book | |
| 12. | 14.10 14:15-16:00 | Evaluation Methods for Classification Models | same slides of the previous lecture | Chap. 3 Kumar Book | |
| 13. | 15.10 09:00-10:45 | Statistical tool for model evaluation + Rule based classification | 10-rule-based-clussifiers.pdf | Chap. 3 Kumar Book + Chap. 4 Kumar Book | |
| 14. | 20.10 14:15-16:00 | Rule based classification + Instance-based Classification | 10-knn.pdf | Chap. 4 Kumar Book | |
| 15. | 21.10 14:15-16:00 | Exercise on DT learning + Naive Bayesian Classifier | 11_2021-naive_bayes.pdf 2021-dt-ex.pdf | Chap. 4 Kumar Book | |
| 16. | 22.10 09:00-10:45 | SVM & Ensemble Classifiers | 14_svm_2020.pdf 13_ensemble_2020.pdf | Chap. 4 Kumar Book | |
| 17. | 27.10 14:15-16:00 | Neural Networks | 15_neural_networks_2021.pdf | Chap. 4 Kumar Book | |
| 18. | 28.10 14:15-16:00 | Python Lab on Classification | adult_classification_2021.ipynb.zip | ||
| 29.11 09:00-10:45 | Canceled | ||||
| 19. | 03.11 14:15-16:00 | Python Lab on Classification + Association Rule Mining | classificationpython2.zip 17_association_analysis2021.pdf | Chap.5 Association Rules: Kumar Book | |
| 20. | 04.11 14:15-16:00 | Association Rule Mining | Chap.5 Association Rules: Kumar Book | ||
| 21. | 05.11 09:00-10:45 | FP-Growth - Sequential Pattern Mining | 17_2021-fp-growth.pdf | Chap.6 Kumar Book | |
| 22. | 10.11 14:15-16:00 | Sequential Pattern Mining | 18_sequential_patterns_2021.pdf | Chap.7 Kumar Book | |
| 23. | 11.11 14:15-16:00 | Time Series Similarities, Transformations & Clustering | 22_time_series_similarity_2021.pdf | Overview on DM for time series | |
| 24. | 12.11 09:00-10:45 | Motif & Shapelet Discovery | 23_time_series_shapelets-motif-2021.pdf | matrixprofile.pdf shaplet.pdf | |
| 25. | 17.11 14:15-16:00 | Lab: Association Rules & Sequential pattern mining by Python | arm-spm.zip | ||
| 26. | 18.11 14:15-16:00 | Ethics & Privacy | 19_ethics_privacy2021.pdf > | Overview on Privacy allegato11-cpdp13.pdf Privacy by design | |
| 27. | 19.11 09:00-10:45 | Lab: Time series | timeseries-py.zip | ||
| 28. | 24.11 14:15-16:00 | Explainability | 20_explainability_2021.pdf | Material: LORE LIME Survey ABELE SHAP LASTS | |
| 29. | 25.11 14:15-16:00 | Explainability + LAB XAI | xai-lab.zip | ||
| 30. | 26.11 09:00-10:45 | LAB XAI + Anomaly Detection | AD&OD | ||
| 31. | 01.12 14:15-16:00 | Anomaly Detection + Lab | ADPY | ||
| 32. | 02.12 14:15-16:00 | CRISP-DM | crisp-dm.pdf | ||
| . | 03.12 09:00-10:45 | Canceled | |||
| 33. | 15.12 14:15-16:00 Room C | Paper Presentation | |||
| 34. | 16.12 14:15-16:00 Room C | Paper Presentation | |||
| 35. | 17.12 09:00-12:45 Room C | Paper Presentation |
Mid-term Project
A project consists in data analyses based on the use of data mining tools. The project has to be performed by a team of 2/3 students. It has to be performed by using Python. The guidelines require to address specific tasks. Results must be reported in a unique paper. The total length of this paper must be max 25 pages of text including figures. The students must deliver both: paper (single column) and well commented Python Notebooks.
Students who did not deliver the above project within 5th Jan 2022 need to ask by email a new project to the teachers. The project that will be assigned will require about 2 weeks of work and after the delivery it will be discussed during the oral exam.
Paper Presentation (OPTIONAL)
Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions. They only need to present the project (see next point). The paper presentation can be done by the group or by a single person.
Oral Exam
… a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them.
Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.