Fundamentals of Machine Learning

KNOWLEDGE AND UNDERSTANDING

The aim of the course is to provide the student with knowledge and skills in the area of machine learning.
At the end of the course the student should be able to:

• distinguish the various machine learning paradigms;
• know the learning theory
• know classification algorithms, regression, clustering and dimension reduction;

APPLYING KNOWLEDGE AND UNDERSTANDING
After completing the course, the student must demonstrate that he is able to:

• apply the different machine learning paradigms
• implement the classification, regression, clustering and dimensionality reduction algorithms;
• design and implement systems able to learn automatically from real data and situations;

COMMUNICATION SKILLS
At the end of this training activity, the student will be able to express himself clearly and with appropriate terms, using the English language, in the learning discussions as well as expose the results of a research concerning technical aspects of machine learning.

LEARNING SKILLS
At the end of this training activity the student will be able to:

• Finding and learning the innumerable algorithms and techniques that are presented in the field of machine learning
• Implementing and using the new algorithms

• Notions of Probability and Linear Algebra

• Supervised Learning

• Linear Models and Regression for Classification

• Decision Tree, Random Forest, Ensemble

• Unsupervised Learning

• Dimensionality Reduction

• Clustering

• Neural networks (an introduction) : FFNN, RNN, SOM

• Balanced and unbalanced data

• Evaluation Metrics (AUC, ROC, Confusion Matrix…)

• Examples of coding in Python

• Probabilistic learning theory and the “learning problem”

• The VC-dimension

• Bias-Variance Trade-off

• Support Vector Machines (Proof of the maximum margin)

Course Slides and video

• 03/10/2022 Lezione1
• 04/10/2022 Lezione2
• 10/10/2022 Please refer to 04/10/2022
• 11/10/2022 Please refer to 04/10/2022
• 17/10/2022 Please refer to 04/10/2022
• 18/10/2022 Please refer to 04/10/2022
• 24/10/2022 Lezione7
• 25/10/2022 Please refer to 24/10/2022
• 07/11/2022 Lezione9 (NNs)
• 08/11/2022 Please refer to 07/11/2022
• 14/11/2022 Lezione11
• 15/11/2022 Please refer to 14/11/2022
• 21/11/2022 Lezione13
• 22/11/2022 Cancelled
• 28/11/2022 Cancelled
• 29/11/2022 Cancelled
• 05/12/2022 Cancelled
• 06/12/2022 Cancelled
• 12/12/2022 Lezione14---CLustering
• 13/12/2022 Lezione15
• 19/12/2022 Please refer to 13/12/2022
• 20/12/2022 Please refer to 13/12/2022
• 09/01/2023 Please refer to 13/12/2022
• 10/01/2023 Lezione19
• 16/01/2023 Lezione20
• 17/01/2023 Lezione21

Reference books

• Norvig, P., Russel, S., Artificial Intelligence: a Modern Approach - Pearson, 2021
• Sutton, R., Barto, A. Reinforcement Learning: an Introduction Online version
• C.M. Bishop, Pattern Recognition and Machine Learning, Springer - 2006
• D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press. - 2012 Online version
• T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer - 2008 Online version

Link to recordings Recorded lessons

Telegram https://t.me/+Cg6G5oi50qozMzk8

Other sources Some of the material is taken from the ML Course of Caltech given by Prof. Yaser Abu Mostafa and his collaborators

Additional Material

• 12/10/2022 Math for Machine Learning - no Bayesian Probability
• 13/11/2022 BackPropagation notes
• 02/01/2023 Growth Function notes
• 11/10/2020 Vapnik paper
• 11/10/2020 Hoeffding's
• 28/09/2019 Optimization_notes
• 28/09/2019 SVM1
• 28/09/2019 SVM2
• 28/09/2019 SVM3
• 12/11/2019 Explaining AdaBoost- Schapire
• 12/11/2019 Ensemble Methods - Zhou
• 12/12/2019 Bias-Variance I
• 12/12/2019 Bias-Variance II
• 16/12/2019 Vapnik's Inequality

Exam Dates A.Y. 2022/2023

• Monday, February 6, 2023 (9:00-12:30 - Room AB1)
• Monday, February 27, 2023 (ore 9:00-12:30 - Room AB1)
• Monday, June 26, 2023 ore (9:00-12:30 - Room AB1)
• Monday, July 17, 2023 ore (9:00-12:30 - Room AB1)
• Monday, September 4, 2023 (ore 9:00-12:30 - Room AB1)
• Monday, September 18, ore (9:00-12:30 - Room AB1) –> TBC

Please, once inside your ESSE3 private account, select the dates with the labels in the following form …

Exam rules: The exam consists in a test (approximately 2.5 hours) where you have to answer theoretical questions and solve practical exercises. It is allowed to use calculator.