Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence

A.Y. 2020/2021
12
Max ECTS
80
Overall hours
SSD
INF/01 SECS-S/01
Language
English
Learning objectives
The course introduces students to the most important algorithmical and statistical machine learning tools. The first part of the course focuses on the statistical foundations and on the methodological aspects. The second part is more hands-on, with laboratories to help students develop their software skills.
Expected learning outcomes
Upon completion of the course students will be able to:
1. understand the notion of overfitting and its role in controlling the statistical risk
2. describe some of the most important machine learning algorithms and explain how they avoid overfitting
3. run machine learning experiments using the correct statistical methodology
4. provide statistical interpretations of the results.
Course syllabus and organization

Single session

Responsible
Lesson period
Second semester
In case the Covid emergency prevents lectures from being given in class, these will be delivered live via the Zoom platform (Module Machine Learning) or Teams according to the regular schedule. Each live lecture will be video recorded and immediately made available to all students via a link (Module Machine Learning) or in the platform. The teaching modality (in class vs. online) and the instructions for attending classes will be advertised on the course web page.

The syllabus and the reference material will not change.

The methods of assessment and the evaluation criteria will not change. However, exams may take place via Zoom or Teams depending on the rules being enforced at the time of the exam session.
Prerequisites for admission
The course requires basic knowledge in calculus, linear algebra, programming and statistics.
Assessment methods and Criteria
For the module Machine learning, the exam consists in writing a paper of about 10-15 pages containing either a report describing experimental results (experimental project) or a in-depth analysis of a theoretical topic (theory project). The paper will be discussed in an oral examination, in which students will be also asked questions on the rest of the syllabus. The final grade is computed by combining the project evaluation and the oral discussion. As a function of the number of students attending the course, the oral discussion may be replaced by a written test.

For the Module Statistical Learning, Deep Learning and Artificial Intelligence, the exam consists in preparing two assigments, using the package R, assigned during the course. The assignments will be discussed in an oral examination, in which students will be asked to explain and dicuss the methodological choices and the code. The grade is computed by combining the assigments evaluation and the oral examination.

The final grade is the mean of the grades obtained in each module.
Module Machine Learning
Course syllabus
1. Introduction
2. The Nearest Neighbour algorithm
3. Tree predictors
4. Statistical learning
5. Hyperparameter tuning and risk estimates
6. Risk analysis of Nearest Neighbour
7. Risk analysis of tree predictors
8. Consistency, surrogate functions, nonparametric algorithms
9. Linear predictors
10. Online gradient descent
11. From sequential risk to statistical risk
12. Kernel functions
13. Support Vector Machines
14. Stability bounds and risk control for SVM
15. Boosting and ensemble methods
16. Neural networks and deep learning
Teaching methods
Lectures

The goal of this course is to provide a methodological foundation to machine learning. The emphasis is on the design and analysis of learning algorithms with theoretical performance guarantees.
Teaching Resources
The main reference are the lecture notes available through the link ncesa-bianchismml.ariel.ctu.unimi.it/

A further reference is the textbook: Shai Shalev-Shwartz e Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
Module Statistical Learning, Deep Learning and Artificial Intellingence
Course syllabus
1. Introduction to Statistical Learning
2. Cross Validation and Bootstrap
3. Variable Selection, Ridge and Lasso Regression
4. Linear Models
5. Non Linear Models
6. Logistic Regression and classification Methods
7. Classification and Regression Trees, bagging, boosting and Random Forest
8. Unsupervised learning (Clustering, PCA)
9. Brief notes on neural networks (tentative)
10. Brief notes on the association rules (tentative)
Teaching methods
Lectures and Lab sessions

The goal of this module is to provide a methodological and practical overview to statistical learning methods. The emphasis is on the applications.
Teaching Resources
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning, Springer.

A further reference is the textbook:
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
Module Machine Learning
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Module Statistical Learning, Deep Learning and Artificial Intellingence
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Professor: Salini Silvia
Professor(s)
Reception:
Wednesday 9:30AM-12:30PM
39, via Comelico. Room P101
Reception:
Tuesdays from 10 to 13. CORONAVIRUS EMERGENCY: The teacher is available to conduct interviews via Teams or Skype. First send a mial to make an appointment.
DEMM, room 31, 3° floor