Statistical Methods for Machine Learning
A.Y. 2019/2020
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
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.
Introduction
The Nearest Neighbour algorithm
Tree predictors
Statistical learning
Cross validation
Risk analysis of Nearest Neighbour
Risk analysis of tree predictors
Consistency and nonparametric algorithms
Linear classification
Online gradient descent
From sequential risk to statistical risk
Kernel functions
Support Vector Machines
Stability bounds and risk control in SVM
Boosting
Logistic regression
Compression bounds
Neural networks and deep learning
Introduction
The Nearest Neighbour algorithm
Tree predictors
Statistical learning
Cross validation
Risk analysis of Nearest Neighbour
Risk analysis of tree predictors
Consistency and nonparametric algorithms
Linear classification
Online gradient descent
From sequential risk to statistical risk
Kernel functions
Support Vector Machines
Stability bounds and risk control in SVM
Boosting
Logistic regression
Compression bounds
Neural networks and deep learning
Prerequisites for admission
The course requires basic knowledge in calculus, linear algebra, and statistics.
Before attending this course, students are strongly advised to take the folowing exams: Continuum mathematics, Discrete mathematics, Statistics and data analysis.
Before attending this course, students are strongly advised to take the folowing exams: Continuum mathematics, Discrete mathematics, Statistics and data analysis.
Teaching methods
Lectures.
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.
A further reference is the 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 a in-depth analysis of a theoretical topic (theory project). The paper will be discussed in an oral examination, in which students will be asked detailed questions about the algorithms used in the project, and also more high-level questions on the rest of the syllabus. The final grade is computed by combining the project evaluation and the oral discussion.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Cesa Bianchi Nicolo' Antonio
Shifts:
-
Professor:
Cesa Bianchi Nicolo' AntonioProfessor(s)