digitalhealth:0001a
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| digitalhealth:0001a [25/11/2025 alle 10:50 (3 settimane fa)] – [First Semester] Anna Monreale | digitalhealth:0001a [16/12/2025 alle 16:13 (2 giorni fa)] (versione attuale) – [Exams] Anna Monreale | ||
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| * The slides used in the course will be inserted in the calendar after each class. Most of them are part of the slides provided by the textbook' | * The slides used in the course will be inserted in the calendar after each class. Most of them are part of the slides provided by the textbook' | ||
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| + | ===== Past Excercises and past exams of similar courses | ||
| + | * Exercises on Clustering: {{ : | ||
| + | * Excercise for DT learning simulation: {{ : | ||
| + | * Some text of past exams of a similar course: {{ : | ||
| + | * Some very old exercises (part of them with solutions) are available here, most of them in Italian, not all of them on topics covered in this year program: {{tdm: | ||
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| ====== Class Calendar (2025/2026) ====== | ====== Class Calendar (2025/2026) ====== | ||
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| ===== First Semester | ===== First Semester | ||
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| |19. | 07.11 |Project work | | |Monreale, Naretto | | |19. | 07.11 |Project work | | |Monreale, Naretto | | ||
| |20. | 10.11 |KNN and Logistic regression | |20. | 10.11 |KNN and Logistic regression | ||
| - | |21. | 11.11 |LG |{{ : | + | |21. | 11.11 |LG |{{ : |
| |22. | 14.11 |Rule-based classifier |{{ : | |22. | 14.11 |Rule-based classifier |{{ : | ||
| |23. | 17.11 | Naive Bayes | {{: | |23. | 17.11 | Naive Bayes | {{: | ||
| - | |24. | 18.11 | Naive Bayes and Ensemble methods| {{: | + | |24. | 18.11 | Naive Bayes and Ensemble methods| {{: |
| |25. | 21.11 | Ensemble methods | {{: | |25. | 21.11 | Ensemble methods | {{: | ||
| |26. | 24.11 |Imbalanced learning | |26. | 24.11 |Imbalanced learning | ||
| - | |27. | 25.11 | | | | | | + | |27. | 25.11 | Python Lab on classification and presentation of the project, task 4|{{: |
| - | |28. | 28.11 | | | | | | + | |28. | 28.11 |GSP and Apriori |
| - | |29. | 01.12 | | | | | | + | |29. | 01.12 |GSP and Apriori |
| - | |30. | 02.12 | | | | | | + | |30. | 02.12 |Time series |
| - | |31. | 05.12 | | | | | | + | |31. | 05.12 |Time series lab |{{: |
| - | |32. | 09.12 | | | | | | + | |32. | 09.12 |Anomaly detection tabular data |{{: |
| - | |33. | 12.12 | | | | | | + | | | 12.12 |Strike |
| - | |34. | 15.12 | | | | | | + | |33. | 15.12 |Anomaly detection ts + Python lab |{{: |
| - | |35. | 16.12 | | | | | | + | |34. | 16.12 | | | | | |
| - | |36. | 19.12 | Project CHECK - mandatory | | | | | + | |35. | 19.12 | Project CHECK - mandatory | | | | |
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| A project consists in data analyses based on the use of data mining tools. | 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 max 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. | The project has to be performed by a team of 2 max 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. | ||
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| + | Deadline. January 5th, 2026. | ||
| + | Delivery instructions. The final deadline of the project is 5th January 2026 at 23:59. This deadline is STRICT. No extension is possible because then the winter session of exams starts. Groups that will not deliver the project by 5th January will need to do the written exam during the exam sessions. Each group must deliver by email to anna.monreale@unipi.it, | ||
| ====== Previous years ===== | ====== Previous years ===== | ||
digitalhealth/0001a.1764067833.txt.gz · Ultima modifica: 25/11/2025 alle 10:50 (3 settimane fa) da Anna Monreale
