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magistraleinformatica:dmi:start [24/11/2023 alle 19:31 (5 mesi fa)]
Anna Monreale [First Semester] |
magistraleinformatica:dmi:start [22/03/2024 alle 20:34 (5 settimane fa)] (versione attuale)
Anna Monreale [First Semester] |
| |6. | 11.10 | Introduction to Clustering. Centroid-based Clustering: K-means algorithm. | {{ :magistraleinformatica:dmi:5-basic_cluster_analysis-intro.pdf |}} {{ :magistraleinformatica:dmi:6.1-basic_cluster_analysis-kmeans.pdf |}} | Chap. 7 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EV-fDd75MIxGmazA79kFHCYBI78yYwqy7AFE5h9MN2rRqg?e=YVgdjS|Video 1: Introduction to Clustering + K-means - Part 1]] - Video of previous years| | |6. | 11.10 | Introduction to Clustering. Centroid-based Clustering: K-means algorithm. | {{ :magistraleinformatica:dmi:5-basic_cluster_analysis-intro.pdf |}} {{ :magistraleinformatica:dmi:6.1-basic_cluster_analysis-kmeans.pdf |}} | Chap. 7 Kumar Book | [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EV-fDd75MIxGmazA79kFHCYBI78yYwqy7AFE5h9MN2rRqg?e=YVgdjS|Video 1: Introduction to Clustering + K-means - Part 1]] - Video of previous years| |
| |7. | 12.10 | Centroid-based Clustering: K-means variants. | {{ :magistraleinformatica:dmi:6.2-basic_cluster_analysis-kmeans-variants.pdf |}} | Chap. 7 Kumar Book {{ :magistraleinformatica:dmi:clusteringmixturemodels.pdf |}} {{ :magistraleinformatica:dmi:xmeans.pdf |}}| [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ETySd1UWIzxCoAKilzaXO_MBW8oXZZCjf5FEhyywGIdJBg?e=Xq2jdo|Video 2: Introduction to Clustering + K-means - Part 2]]] [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQTbbvqF2kJOgEsFQ1WF48cBjWf2wgTCbOjxcQzn9MyVzw?e=KQ7gEZ|Video 1: Center-based clustering - Bisecting K-means, Xmeans, EM ]];Videos of previous years| | |7. | 12.10 | Centroid-based Clustering: K-means variants. | {{ :magistraleinformatica:dmi:6.2-basic_cluster_analysis-kmeans-variants.pdf |}} | Chap. 7 Kumar Book {{ :magistraleinformatica:dmi:clusteringmixturemodels.pdf |}} {{ :magistraleinformatica:dmi:xmeans.pdf |}}| [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/ETySd1UWIzxCoAKilzaXO_MBW8oXZZCjf5FEhyywGIdJBg?e=Xq2jdo|Video 2: Introduction to Clustering + K-means - Part 2]]] [[https://unipiit.sharepoint.com/:v:/s/a__td_54794/EQTbbvqF2kJOgEsFQ1WF48cBjWf2wgTCbOjxcQzn9MyVzw?e=KQ7gEZ|Video 1: Center-based clustering - Bisecting K-means, Xmeans, EM ]];Videos of previous years| |
| | | 13.10 | Suspension of teaching | | | | | | | 13.10 | Suspension of teaching | | | Recording in Teams Channel | |
| |8.| 18.10 | Hierarchical and density based CLustering | {{ :magistraleinformatica:dmi:7.basic_cluster_analysis-hierarchical.pdf |}} {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} | Chap. 7 Kumar Book | | | |8.| 18.10 | Hierarchical and density based CLustering | {{ :magistraleinformatica:dmi:7.basic_cluster_analysis-hierarchical.pdf |}} {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} | Chap. 7 Kumar Book | Recording in Teams Channel | |
| |9.| 19.10 | Clustering Validity & Python Lab: Clusterig K-means | {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} | Chap. 7 Kumar Book| | | |9.| 19.10 | Clustering Validity & Python Lab: Clusterig K-means | {{ :magistraleinformatica:dmi:8.basic_cluster_analysis-dbscan-validity.pdf |}} | Chap. 7 Kumar Book| Recording in Teams Channel | |
| |10.| 20.10 | Python Lab: Clusterig Density based and hierarchical + Introduction to Classification |{{ :magistraleinformatica:dmi:clustering.zip | Notebook on Clustering}} {{ :magistraleinformatica:dmi:9.chap3_basic_classification-2023.pdf |}} | Chap.3 Kumar Book | | | |10.| 20.10 | Python Lab: Clusterig Density based and hierarchical + Introduction to Classification |{{ :magistraleinformatica:dmi:clustering.zip | Notebook on Clustering}} {{ :magistraleinformatica:dmi:9.chap3_basic_classification-2023.pdf |}} | Chap.3 Kumar Book |Recording in Teams Channel | |
| |11.| 25.10 | Decision Trees & Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book | | | |11.| 25.10 | Decision Trees & Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book | Recording in Teams Channel | |
| |12.| 26.10 | Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book | | | |12.| 26.10 | Classifier Evaluation | Same slides as previous lecture | Chap.3 Kumar Book | | |
| |13.| 27.10 | Rule-based Classifiers |{{ :magistraleinformatica:dmi:10-rule-based-classifiers.pdf |}} | Chap.4 Kumar Book | | | |13.| 27.10 | Rule-based Classifiers |{{ :magistraleinformatica:dmi:10-rule-based-classifiers.pdf |}} | Chap.4 Kumar Book | Recording in Teams Channel | |
| |14.| 02.11 | Rule-based Classifiers + Instance based Classifiers| {{ :magistraleinformatica:dmi:10-knn.pdf |}}| Chap.4 Kumar Book | | | |14.| 02.11 | Rule-based Classifiers + Instance based Classifiers| {{ :magistraleinformatica:dmi:10-knn.pdf |}}| Chap.4 Kumar Book | Recording in Teams Channel | |
| |15.| 03.11 |Naive Bayesian Classifier. SVM. Ensemble Classifiers| {{ :magistraleinformatica:dmi:11_2023-naive_bayes.pdf |}} {{ :magistraleinformatica:dmi:14_svm_2023.pdf |}} {{ :magistraleinformatica:dmi:13_ensemble_2023.pdf |}}| Chap.4 Kumar Book | | | |15.| 03.11 |Naive Bayesian Classifier. SVM. Ensemble Classifiers| {{ :magistraleinformatica:dmi:11_2023-naive_bayes.pdf |}} {{ :magistraleinformatica:dmi:14_svm_2023.pdf |}} {{ :magistraleinformatica:dmi:13_ensemble_2023.pdf |}}| Chap.4 Kumar Book | Recording in Teams Channel | |
| |16.| 08.11 | Python Lab: Classification| {{ :magistraleinformatica:dmi:classification.zip |}} | | | | |16.| 08.11 | Python Lab: Classification| {{ :magistraleinformatica:dmi:classification.zip |}} | | Recording in Teams Channel | |
| |17.| 09.11 | NN Classifiers| {{ :magistraleinformatica:dmi:15_neural_networks_2023.pdf |}} | Chap.4 Kumar Book | | | |17.| 09.11 | NN Classifiers| {{ :magistraleinformatica:dmi:15_neural_networks_2023.pdf |}} | Chap.4 Kumar Book | Recording in Teams Channel | |
| |18.| 10.11 | Python Lab: NN & Imbalanced Classification | {{ :magistraleinformatica:dmi:imbalanced_classification.zip |}} | | | | |18.| 10.11 | Python Lab: NN & Imbalanced Classification | {{ :magistraleinformatica:dmi:imbalanced_classification.zip |}} | | Recording in Teams Channel | |
| |19.| 15.11 | Association Rule Mining: Apriori | {{ :magistraleinformatica:dmi:17_association_analysis.pdf |}} | Chap.5 Kumar Book | | | |19.| 15.11 | Association Rule Mining: Apriori | {{ :magistraleinformatica:dmi:17_association_analysis.pdf |}} | Chap.5 Kumar Book | Recording in Teams Channel | |
| |20.| 16.11 | Association Rule Mining: Evalaution and FP-Growth | {{ :magistraleinformatica:dmi:17_2023-fp-growth.pdf |}} | Chap.5 Kumar Book | | | |20.| 16.11 | Association Rule Mining: Evalaution and FP-Growth | {{ :magistraleinformatica:dmi:17_2023-fp-growth.pdf |}} | Chap.5 Kumar Book | Recording in Teams Channel | |
| |21.| 17.11 | Sequential Pattern Mining | {{ :magistraleinformatica:dmi:18_sequential_patterns_2023.pdf |}} | Chap.6 Kumar Book | | | |21.| 17.11 | Sequential Pattern Mining | {{ :magistraleinformatica:dmi:18_sequential_patterns_2023.pdf |}} | Chap.6 Kumar Book | Recording in Teams Channel | |
| |22.| 22.11 | Sequential Pattern Mining: timing constraint. Time Series Analysis: Similarities, Distances and Transformations| {{ :magistraleinformatica:dmi:22_time_series_similarity_2023.pdf |}} | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf |Overview on Time Series]] | | | |22.| 22.11 | Sequential Pattern Mining: timing constraint. Time Series Analysis: Similarities, Distances and Transformations| {{ :magistraleinformatica:dmi:22_time_series_similarity_2023.pdf |}} | [[https://cs.gmu.edu/~jessica/BookChapterTSMining.pdf |Overview on Time Series]] | Recording in Teams Channel | |
| |23.| 23.11 | Time Series Analysis: Shapelet & Motif| {{ :magistraleinformatica:dmi:23_time_series_motif-shapelets2023.pdf |}} | | | | |23.| 23.11 | Time Series Analysis: Shapelet & Motif| {{ :magistraleinformatica:dmi:23_time_series_motif-shapelets2023.pdf |}} | {{ :magistraleinformatica:dmi:shaplet.pdf |}} | Recording in Teams Channel | |
| |24.| 24.11 | Time Series Analysis: Shapelet & Motif| | {{ :magistraleinformatica:dmi:matrixprofile.pdf |}} | | | |24.| 24.11 | Time Series Analysis: Shapelet & Motif; introduction to ethics and privacy| same slides of the previous lecture and {{ :magistraleinformatica:dmi:19_ethics_privacy_2023_intro.pdf |}} | {{ :magistraleinformatica:dmi:matrixprofile.pdf |}} [[https://www.cs.ucr.edu/~eamonn/MatrixProfile.html|Papers and resourse on motif]] | Recording in Teams Channel | |
| | |25.| 29.11 | Python Lab: ARM, SPM, Time series transformations | {{ :magistraleinformatica:dmi:ar_spm.zip |}} {{ :magistraleinformatica:dmi:timeseries.zip |}} | | Recording in Teams Channel | |
| | |26.| 30.11 | Python Lab: Time series analysis | notebooks in the zip file of the previous lecture| | Recording in Teams Channel | |
| | |27.| 01.12 | Privacy in AI and Big Data Analytics | {{ :magistraleinformatica:dmi:19_ethics_privacy2023.pdf |}} This set of slides include alse the introduction of the lecture 24.11.2023 |{{ :magistraleinformatica:dmi:chap-anonymity.pdf |}} {{ :magistraleinformatica:dmi:chap-anonymity.pdf |}} {{ :magistraleinformatica:dmi:prudence.pdf |}} {{ :magistraleinformatica:dmi:chapter-ppdm.pdf |}}| Recording in Teams Channel | |
| | |28.| 06.12 | Explainable AI | {{ :magistraleinformatica:dmi:20_explainability_2023.pdf |}}|{{ :magistraleinformatica:dmi:lore-tabular.pdf |}} {{ :magistraleinformatica:dmi:xai-survey.pdf |}} {{ :magistraleinformatica:dmi:imagexai.pdf |}} {{ :magistraleinformatica:dmi:timeseriesxai.pdf |}}| Recording in Teams Channel | |
| | |29.| 07.12 | Explainable AI | {{ :magistraleinformatica:dmi:21_anomaly_detection_2023.pdf |}} {{ :magistraleinformatica:dmi:anomaly_detection.zip |}}| | Recording in Teams Channel | |
| | |30.| 13.12 | Anomaly Detection | {{ :magistraleinformatica:dmi:21_anomaly_detection_2023.pdf |}} | | Recording in Teams Channel | |
| | |31-32.| 14.12 9-11| Lab Python in AD + Lab Python in XAI| {{ :magistraleinformatica:dmi:anomaly_detection.zip |}}| | Recording in Teams Channel | |
| | |33.| 15.12 9-11| Lab Python in XAI + Paper Presentation| | | | |
| | |34.| 18.12 09-11| Paper Presentation| | | | |
| | |35.| 20.12 09-11| Paper Presentation| | | | |
| | |36.| 21.12 09-11| Paper Presentation| | | | |
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| - **Deadline**: Jan 8, 2024 | - **Deadline**: Jan 8, 2024 |
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| * Third part of the project consists in the assignment described here: | * Third part of the project consists in the assignment described here: {{ :magistraleinformatica:dmi:project_description_dm23-pub-complete.pdf |Updated Project Description}} |
| - **Deadline**: Jan 8, 2024 | - **Deadline**: Jan 8, 2024 |
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| **Students who did not deliver the above project within **Jan 8, 2024** 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. ** | **Students who did not deliver the above project within **Jan 8, 2024** need to ask by email a new project to the teachers. The project that will be assigned will require about 20 days of work and after the delivery it will be discussed during the oral exam. ** |
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| ** Paper Presentation (OPTIONAL)** | ** Paper Presentation (OPTIONAL)** |
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| 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. | 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 on the entire program. They only need to present the project (see next point) and answer open question only on the topics which will not be covered by the project. The paper presentation can be done by the group or by a single person. |
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| **Oral Exam** | **Oral Exam** |
| * **Project presentation** (with slides) – 10-15 minutes: mandatory for all the students with question fo understanding the details of any part of the project. | * **Project presentation** (with slides) – 10-15 minutes: mandatory for all the students with question fo understanding the details of any part of the project. |
| * ** Open questions on the entire program **: for students who will not opt for paper presentation | * ** Open questions on the entire program **: for students who will not opt for paper presentation |
| * ** Open questions on the topics which will not be covered by the project ** only for students opting for paper presentation. | * ** Open questions on the topics which will not be covered by the project ** only for students opting for paper presentation. |
| | * Group presentations of the project are preferred. If this is impossible please contact me for finding a solution. |
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| | **How to book for the exam colloquium? ** |
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| | In https://esami.unipi.it/ you can find the dates for the exam: one for January and one for February. Each student must do the registration on one of the 2 dates. These are not the dates of the colloquium or project delivery but we will use the list of registered students for organizing the exam dates. After that deadline we will share with you a calendar for the oral exam. |
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