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

• Clustering

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

• 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

• 02/10/2023 Lezione1
• 03/10/2023 Lezione2
• 09/10/2023 —— Look at 03/10/2023
• 10/10/2023 —— Look at 03/10/2023
• 16/10/2023 Lezione3
• 17/10/2023 —— Look at 16/10/2023
• 23/10/2023 Lezione4
• 30/10/2023 Lezione5
• 31/10/2023 —— Look at 30/10/2023
• 06/11/2023 Lezione6
• 07/11/2023 Lezione7
• 13/11/2023 —— Look at 07/11/2023
• 14/11/2023 —— Look at 07/11/2023
• 20/11/2023 Evaluation Strategies
• 21/11/2023 Lezione8
• 27/11/2023 —— Look at 21/11/2023
• 28/11/2023 —— Look at 21/11/2023
• 04/12/2023 —— Look at 21/11/2023
• 05/12/2023 Lezione9
• 11/12/2023 Lezione10
• 12/12/2023 Lezione11

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/+1ij_W3mEca9iNDc0

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

• 02/10/2023 Math for Machine Learning - no Bayesian Probability
• 26/10/2023 Regression
• 30/10/2023 DataSet Decision Tree - Download the file and change the extension from .pdf to .png
• 30/10/2023 Ensemble Book
• 30/10/2023 AdaBoost Paper
• 20/11/2023 Notes about Hoeffding's Inequality

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Exams