Statistical Methods for Machine Learning
A.Y. 2018/2019
Learning objectives
Machine learning is concerned with the design of algorithms that can predict the evolution of a phenomenon based of a set of observations. Machine learning is a standard tool in the development of intelligent systems. It has been successfully applied to a wide range of domains, including vision, human-computer interaction, product recommendation, health, autonomous navigation, and many more. The course will describe and analyze, in a rigorous statistical framework, the most important machine learning techniques. This will provide the student with a rich set of methodological tools for understanding the general phenomenon of learning in machines.
Expected learning outcomes
Undefined
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
Milan
Responsible
Lesson period
Second semester
Course syllabus
Introduction
Nearest Neighbour
Tree predictors
Cross validation
Statistical risk
Risk analysis for tree predictors
Risk analysis for Nearest Neighbor
Consistency
Compression bounds
Linear classifiers
Online gradient descent
From sequential risk to statistical risk
Kernel functions
Support Vector Machines
Stability bounds and risk in SVM
Boosting
Neural networks and deep learning
Dimensionality reduction
Nearest Neighbour
Tree predictors
Cross validation
Statistical risk
Risk analysis for tree predictors
Risk analysis for Nearest Neighbor
Consistency
Compression bounds
Linear classifiers
Online gradient descent
From sequential risk to statistical risk
Kernel functions
Support Vector Machines
Stability bounds and risk in SVM
Boosting
Neural networks and deep learning
Dimensionality reduction
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Cesa Bianchi Nicolo' Antonio
Professor(s)