This is an old revision of the document!
Machine Learning
News
- 02/10/2018: Starting date
- 11/10/2018: Class on Tuesday 16/10/2018 will not take place
- 18/10/2018: This morning 18/10/2018 class will not take place
- 11/10/2018: Classes on Wednesdays are moved to Thursdays from 9:00 to 11:00 - Polo Lodovici, Room Lab2
- Slides form classes 3,4,5,6 and 7 have been added
- 14/11/2018 Class on 15/11/2018 will not take place. Next lesson TUESDAY 20/11/2018
- 22/11/2018 Class on 27/11/2018 and 29/11/2018 will not take place.
- 17/12/2018 Class on 18/12/2018 will not take place due to the persistence of bad weather conditions.
- 18/12/2018 Class of 18/12/2018 will be recovered tomorrow 19/12/2018 from 14:00 to 17:00.
General Info
Teacher:
ESSE3 Link
Class schedule:
- Tuesday 11:00 - 13:00 - AB1
- Thursday 9:00 - 11:00 - LAB2
Students Office hours:
- Thursday from 11:00 to 13:00
Course Objectives
The course intends blablabla….
Course Contents
- Learning theory and the “learning problem”
- The VC-dimension
- 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
Study material
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
Exams
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
Introduction to Machine Learning (for Ph.D.)
Syllabus
- Learning Probabilistic Theory
- Learning paradigms
- Classification
- Clustering
- Neural Networks
- Support Vector Machines
- Practical issues for ML
Material
