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.

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.

• Matteo Matteucci: the course teacher
• Marco Cannici: the teaching assistant

Course Program

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

Date	Day	Time	Room	Teacher	Topic
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)

Course Evaluation

The course evaluation is composed by two parts:

• HW: Homework with exercises covering the whole program
• WE: A written examination covering the whole program