Machine Learning Bio

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The following are last minute news you should be aware of ;-)

* 11/03/2020: The course starts today!
* 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.

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

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)

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

Course Evaluation

The course evaluation is composed by two parts:

  • HW: Homework with exercises covering the whole program (up to 6 points)
  • WE: A written examination covering the whole program (up to 26 points)

the final score will be the sum of HW (not compulsory) and WE scores.

Teaching Material (the textbook)

Lectures will be based on material taken from the book.

If you are interested in a more deep treatment of the topics you can refer to the following book from the same authors

Some additional material that could be used to prepare the oral examination will be provided together with the past homeworks.

Teacher Slides

In the following you can find the lecture slides used by the teacher and the teaching assistants during classes.


  • [2020] Course introduction: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised and unsupervised learning. Regression, classification, clustering terminology and examples.
  • [2020] Statistical Learning Introduction: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)

Additional Resources

Papers and links useful to integrate the textbook

  • Bias vs. Variance: "Understanding the Bias-Variance Tradeoff" essay by Scott Fortmann-Roe
  • Karush Kuhn Tucker Conditions: a short note on their meaning with references to relevant wikipedia pages
  • Seeing Theory: a website where the basic concepts of probability and statistics are explained in a visual way.

Online Resources

The following are links to online sources which might be useful to complement the material above

  • Statistical Learning MOOC covering the entire ISL book offered by Trevor Hastie and Rob Tibshirani. Start anytime in self-paced mode.
  • MATH 574M University of Arizona Course on Statistical Machine Learning and Data Mining; here you can find slides covering part of the course topics (the reference book for this course is again The Elements of Statistical Learning)