Machine Learning

Teacher:

• Marco Piangerelli

ESSE3 Link

• Machine Learning - AY 2018/2019

Class schedule:

• Tuesday 11:00 - 13:00 - AB1
• Thursday 9:00 - 11:00 - LAB2

Students Office hours:

• Thursday from 11:00 to 13:00

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

• Probabilistic learning theory and the “learning problem”

• The VC-dimension (Proof of the maximum margin)

• Notions of Probability and Linear Algebra

• Linear Models and Regression for Classification

• Dimensionality Reduction

• Clustering

• Neural networks (an introduction)

• Decision Tree, Random Forest, Ensemble

• Balanced and unbalanced data

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

• Examples of coding in Python

Course Slides

• 02/10/2018 Lezione0
• 03/10/2018 Lezione1
• 09/10/2018 Lezione2
• 10/10/2018 Lezione3
• 23/10/2018 Lezione4
• 25/10/2018 Lezione5
• 30/10/2018 - 06/11/2018 Lezione6
• 8/11/2018 Lezione7
• 13/11/2018 Lezione 8
• 15/11/2018 Lezione 9 (Per usare il file, scaricare il file come allegato e cambiare l'estensione da .pdf a .py)
• 04/12/2018 Lezione 10-11
• 06/12/2018, 11/12/2018 and 13/12/2018Lezione 12
• 19/12/2018 (3 hr) and 20/12/2018 Lezione 13-14
• 08/01/2019 Lezione 15
• 10/01/2019 Lezione 16
• 15/01/2019 Lezione 17
• 17/01/2019 Lezione 18

Reference books

• 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

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

• 04/12/2018 Hoeffding's
• 07/01/2019 Optimization_notes
• 28/01/2019 SVM1
• 28/01/2019 SVM2
• 28/01/2019 SVM3

Exam Dates A.Y. 2015/2016

• Winter session dates here
• Summer session dates here
• Autumn session dates here
• Winter session dates here (2016)

Exam rules: The exam consists in two sessions: a written session and an oral one. The oral session is reserved for people who pass the written part with a mark greater or equal to 18.

Exam Results

• N/A

Syllabus

• Learning Probabilistic Theory
• Learning paradigms
• Classification
• Clustering
• Neural Networks
• Support Vector Machines
• Practical issues for ML

Material

• Lesson0 13/06/2019 Lezione0
• Lesson1 09/06/2019 Lezione1
• Lesson2 20/06/2019 Lezione2
• Lesson3 24/06/2019 Lezione3
• Lesson4 27/06/2019 Lezione4
• Lesson5 04/07/2019 Lezione5
• Lesson6 13/07/2019 Lezione6