Machine Learning

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

* 18/09/2017: The course starts today!

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 R language.

Teachers

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

The course is composed by a set of ex-cattedra lectures on specific techniques (e.g., linear regression, linear discriminant analysis, clustering, etc.). Supervised and unsupervised learning are discussed in the framework of classification and clustering problems. The course outline is:

  • Machine Learning and Pattern Classification: the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of Statistical Decision Theory;
  • Linear Regression Techniques: linear methods for regression will be disccussed and compared (e.g., Linear Regression and Ridge Regression).
  • Linear Classification Techniques: linear methods for classification will be presented as the starting point for more complex methods (e.g., Linera Regression on the indicator matrix, Linear and Quadratic Discriminant Analysis, Logistic Regression, Percptron rule and Optimal Separating Hyperplanes, a.k.a., Support Vector Machines)
  • Unsupervised Learning Techniques: the most common approaches to unsupervised learning are described mostly focusing on clustering techniques such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, Jarvis-Patrick, etc.;
  • Model Validation and Selection: model validation and selection are orthogonal issues to all previous techniques; during the course their fundamentals are described and discussed in the framework of linear models for regression (e.g., AIC, BIC, cross-validation, etc. ).

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 Monday, in room V.S8-A, starts at 10:15 (cum tempore), ends at 13:15
* On Tuesday, in room V.S8-B, starts at 08:30 (cum tempore), ends at 10:15
Date Day Time Room Teacher Topic
04/10/2016 Tuesday 08:15 - 10:15 V.S8-B Matteo Matteucci Course Introduction (Ch. 1 ISL)
07/10/2016 Friday 10:15 - 12:15 V.S8-A Davide Eynard Introduction to R (Ch. 2 ISL)
11/10/2016 Tuesday 08:15 - 10:15 V.S8-B Davide Eynard Intro Clustering
14/10/2016 Friday 10:15 - 12:15 V.S8-A Davide Eynard Clustering with exercises
18/10/2016 Tuesday 08:15 - 10:15 V.S8-B Davide Eynard Clustering Advanced
21/10/2016 Friday 10:15 - 12:15 V.S8-A Davide Eynard Exercises on Clustering
25/10/2016 Tuesday 08:15 - 10:15 V.S8-B Matteo Matteucci Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
28/10/2016 Friday 10:15 - 13:15 V.S8-A Matteo Matteucci Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
04/11/2016 Friday 10:15 - 13:15 V.S8-A Matteo Matteucci Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
08/11/2016 Tuesday 08:15 - 10:15 V.S8-B Matteo Matteucci Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
11/11/2016 Friday 10:15 - 13:15 V.S8-A Matteo Matteucci Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
15/11/2016 Tuesday 08:15 - 10:15 V.S8-B Davide Eynard Statistical Decision Theory Exercises (Ch. 2 ISL)
18/11/2016 Friday 10:15 - 13:15 V.S8-A Davide Eynard Exercises on Simple Linear Regression (Ch. 3 ISL)
29/11/2016 Tuesday 08:15 - 10:15 V.S8-B Matteo Matteucci Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
02/12/2016 Friday 10:15 - 13:15 V.S8-A Matteo Matteucci Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
06/12/2016 Tuesday 08:15 - 10:15 V.S8-B Matteo Matteucci Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
13/12/2015 Tuesday 08:15 - 10:15 V.S8-B Davide Eynard Exercises on Linear Regression and Feature Selection
16/12/2016 Friday 10:15 - 13:15 V.S8-A Matteo Matteucci Classification by Linear Discriminant Analysis (Ch. 4 ISL)
20/12/2016 Tuesday 08:15 - 10:15 V.S8-B Davide Eynard Exercises on Classification by Logistic Regression
23/12/2016 Friday 10:15 - 12:15 V.S8-A Davide Eynard Exercises on Classification by Linear Discriminant Analysis
10/01/2017 Tuesday 08:15 - 10:15 V.S8-B Matteo Matteucci Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
13/01/2015 Friday 10:15 - 13:15 V.S8-A Matteo Matteucci Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
17/01/2015 Tuesday 08:15 - 10:15 V.S8-B Matteo Matteucci Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)

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
  • WE: A written examination covering the whole program

the final score will take the MAXIMUM between WE and the combination 0.7*WE + 0.3*HW. In practice

  • the homework can only increase your score
  • the homework can only impact for the 30% of the score
  • the homework is not mandatory

Homeworks

Please keep in mind:

  • the homework is not meant to get more grade, it is intended for you to understand better, i.e., from a practical perspective too, the topics of the course
  • provided you attended the lab lectures, and you have the R environment set up, the homework should require not more than 1 day per part
  • the deadline to turn the homework is the 15th of January 2018 we will grade it when grading your classwork
  • you can make the homework in groups up to 2/3 people

Homework 2017/2018

... will be published by Christmas 2017 ..

Homework 2016/2017

We have published the Homework 2016/2017.

Homework 2015/2016

We have published the Homework 2015/2016.

Homework 2014/2015

We have published the Homework 2014/2015.

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.

Lectures:

  • [2016] 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.
  • [2016] Statistical Learning Introduction: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)
  • [2016] Statistical Learning and Model Assessment: Model Assessment for Regression and Classification, Bias-Variance trade-off, Model complexity and overfitting, K-Nearest Neighbors Classifier vs. Bayes Classifier.
  • [2016] Linear Regression: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.
  • [2016] Linear Classification: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods.
  • [2016] Support Vector Machines: Discriminative vs. generative methids. Hyperplanes learning and Perceptron. Maximum Margin Classifiers. The Kernel trick and Support Vector Machines.

For exercises and lab material please refer to Davide Eynard website.

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.

Past Exams and Sample Questions

For some samples of exams you can check the last year PAMI ones

Online Resources

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

  • 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)


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