Machine Learning Bio
The following are last minute news you should be aware of ;-)
* 03/03/2020: The course is going to start soon ...
Course Aim & Organization
The objective of this course is to give an advanced presentation, i.e., a statistical perspective, of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling. The course will provide the basics of Regression, Classification, and Clustering with practical exercises using the Python language.
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
Techniques from machine and statistical learning are presented from a theoretical (i.e., based on statistics and information theory) and practical perspective through the descriptions of algorithms, the theory behind them, their implementation issues, and few examples from real applications. The course mostly follows the following book which is also available for download in pdf
- An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Detailed course schedule
A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all days we have lectures!!
Note: Lecture timetable interpretation * On Wednesday, in room ..., starts at 014:15 (cum tempore), ends at 17:15 or 18:15 * On Thursday, in room ..., starts at 08:15 (cum tempore), ends at 10:15
|11/03/2020||Wednesday||14:30 - 17:30||Teams Virtual Class||Matteo Matteucci||Course Introduction (Ch. 1 ISL)|
|12/03/2020||Thursday||08:15 - 10:15||Teams Virtual Class||Matteo Matteucci||Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)|
Chapters are intended as complete except for
- Ch.4 ESL: Section 4.5
- Ch.12 ESL: Sections 12.1, 12.2, 12.3
- Ch.9 ISL: Sections 9.1, 9.2, 9.3
The course evaluation is composed by two parts:
- HW: Homework with exercises covering the whole program
- WE: A written examination covering the whole program