Statistical Methods for Machine Learning
A.Y. 2022/2023
Learning objectives
The course describes and analyzes, in a rigorous statistical framework, some of the most important machine learning techniques. This will provide the student with a rich set of conceptual and methodological tools for understanding the general phenomenon of learning in machines.
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
These objectives are measured via a combination of two components: the project report and the oral discussion. The final grade is formed by assessing the project report, and then using the oral discussion for fine tuning.
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
These objectives are measured via a combination of two components: the project report and the oral discussion. The final grade is formed by assessing the project report, and then using the oral discussion for fine tuning.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Introduction
The Nearest Neighbour algorithm
Tree predictors
Statistical learning
Hyperparameter tuning and risk estimates
Risk analysis of tree predictors
Consistency and nonparametric algorithms
Linear predictors
Online gradient descent
Kernel functions
Support Vector Machines
Stability bounds and risk control for SVM
Boosting and ensemble methods
Neural networks and deep learning
Logistic regression and surrogate loss functions
The Nearest Neighbour algorithm
Tree predictors
Statistical learning
Hyperparameter tuning and risk estimates
Risk analysis of tree predictors
Consistency and nonparametric algorithms
Linear predictors
Online gradient descent
Kernel functions
Support Vector Machines
Stability bounds and risk control for SVM
Boosting and ensemble methods
Neural networks and deep learning
Logistic regression and surrogate loss functions
Prerequisites for admission
The course requires basic knowledge in calculus, linear algebra, and statistics.
Before attending this course, students are strongly advised to pass the following exams: Calculus, Discrete mathematics, Statistics and data analysis.
Before attending this course, students are strongly advised to pass the following exams: Calculus, Discrete mathematics, Statistics and data analysis.
Teaching methods
Lecture-style instruction.
Teaching Resources
The main reference material are the lecture notes available through the link ncesa-bianchismml.ariel.ctu.unimi.it/
Reference textbook: Shai Shalev-Shwartz e Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
Reference textbook: Shai Shalev-Shwartz e Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
Assessment methods and Criteria
The exam consists in writing a paper of about 10-15 pages containing either a report describing experimental results (experimental project) or an in-depth analysis of a theoretical topic (theory project). The final grade is computed by combining the project evaluation with the result of a written test on the syllabus covered in class. Depending on the number of students, the written test may be replaced by an oral discussion.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Cesa Bianchi Nicolo' Antonio
Professor(s)