didattica:ay2223:fml:main

Fundamentals of Machine Learning


* 03/10/2021: Starting date

* 12/10/2022: New Material: Math 4 ML

* 13/11/2022: New Material: Notes about Back Propagation


Teacher:

ESSE3 Link

Scheduling of Lectures:

  • Scheduling is available at the following link

Degrees:

Class schedule:

  • Monday 11:00 - 13:00 - AB1
  • Tuesday 16:00 - 18:00 - AB1

Students Office hours:

  • Monday from 14:00 to 16:00
  • Mandatory taking an appointment by email

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…)
  • Markov Chains and Hidden Markov Models
  • Examples of coding in Python
  • Probabilistic learning theory and the “learning problem”
  • The VC-dimension
  • Support Vector Machines (Proof of the maximum margin)
  • Reinforcement Learning

Course Slides and video

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


Exam Dates A.Y. 2022/2023

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.


  • didattica/ay2223/fml/main.txt
  • Last modified: 2022/11/14 13:05
  • by marcop