Pattern Analysis and Machine Intelligence

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

05/03/2013: a new edition of the course starts today!
05/03/2013: grades from the 29/01/2013 exam are available at this link

Course Aim & Organization

The objective of this course is to give an advanced presentation of the techniques most used in artificial intelligence and machine learning for pattern recognition, knowledge discovery, and data analysis/modeling.

Teachers

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

Course Program

Techniques from machine and statistical learning are presented from a theoretical (i.e., 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 follows, at least partially, the outline of The Elements of Statistical Learning book (by Trevor Hastie, Robert Tibshirani, and Jerome Friedman):

  • Machine Learning and Pattern Classification: in this part of the course the general concepts of Machine Learning and Patter Recognition are introduced with a brief review of statistics and information theory;
  • Unsupervised Learning Techniques: the most common approaches to unsupervised learning are described mostly focusing on clustering techniques, rule induction, Bayesian networks and density estimators using mixure models;
  • Supervised Learning Techniques: in this part of the course the most common techniques for Supervised Learning are described: decision trees, decision rules, Bayesian classifiers, hidden markov models, lazy learners, etc.
  • Feature Selection and Reduction: techniques for data rediction and feature selection will be presented with theory and applications
  • Model Validation and Selection: model validation and selection are orthogonal issues to previous technique; during the course the fundamentals are described and discussed (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!!

Date Day Time Room Teacher Topic
05/03/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Course Introduction (Ch. 1)
11/03/2013 Monday 13:15 - 15:15 3.8 Matteo Matteucci Two examples from classification (Ch. 2)
12/03/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Statistical Decision Theory and Bias-Variance trade off (Ch. 2)
18/03/2013 Monday 13:15 - 15:15 3.8 Luigi Malagò Linear Regression Methods (Ch. 2, Ch. 3, (*))
19/03/2013 Tuesday 13:15 - 15:15 4.1 Luigi Malagò Linear Regression Methods (Ch. 2, Ch. 3, (*))
25/03/2013 Monday 13:15 - 15:15 3.8 Luigi Malagò --- CANCELLED ---
26/03/2013 Tuesday 13:15 - 15:15 4.1 Luigi Malagò Linear Regression Methods (Ch. 2, Ch. 3, (*))
01/04/2013 Monday 13:15 - 15:15 3.8 --- No Lecture
02/04/2013 Tuesday 13:15 - 15:15 4.1 --- No Lecture
08/04/2013 Monday 13:15 - 15:15 3.8 Luigi Malagò Linear Regression Methods (Ch. 2, Ch. 3, (*))
09/04/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Discriminating functions, decision boundary and Linear Regression (Ch.4.1, Ch. 4.2)
15/04/2013 Monday 13:15 - 15:15 3.8 Matteo Matteucci Linear Discriminant Analysis (Ch. 4.3)
16/04/2013 Tuesday 13:15 - 15:15 4.1 Luigi Malagò Linear Regression Methods (Ch. 2, Ch. 3, (*))
22/04/2013 Monday 13:15 - 15:15 3.8 --- No Lecture
23/04/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Linear Discriminant Analysis (Ch. 4.3)
29/04/2013 Monday 13:15 - 15:15 3.8 Matteo Matteucci Logistic Regression (Ch.4.4)
30/04/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Perceptron learning
06/05/2013 Monday 13:15 - 15:15 3.8 Davide Eynard Clustering I: Introduction and K-Means
07/05/2013 Tuesday 13:15 - 15:15 4.1 Davide Eynard Clustering II: K-Means Alternatives, Hierarchical, SOM
13/05/2013 Monday 13:15 - 15:15 3.8 Davide Eynard Clustering III: Mixture of Gaussians, DBSCAN, Jarvis-Patrick
14/05/2013 Tuesday 13:15 - 15:15 4.1 Davide Eynard Clustering IV: Spectral Clustering
20/05/2013 Monday 13:15 - 15:15 3.8 Davide Eynard Clustering V: Evaluation Measures
21/05/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Kernel Smoothing Methods and Kerned Density Estimation (Ch.6.1, Ch.6.6, Ch.6.9)
27/05/2013 Monday 13:15 - 15:15 3.8 Matteo Matteucci Gaussian Mixture Models (Ch.6.8) and the EM Algorithm (Ch.8.5)
28/05/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Decision Trees (handout + Ch. 9.2)
03/06/2013 Monday 13:15 - 15:15 3.8 Matteo Matteucci Perceptron Learning and Support Vector Machines (Ch 4.5)
04/06/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Maximum margin classification (Ch. 4.5.2)
10/06/2013 Monday 13:15 - 15:15 3.8 Matteo Matteucci Support Vector Machines (Ch. 12.1, Ch. 12.2, Ch. 12.3.0, Ch. 12.3.1 + SVM paper)
11/06/2013 Tuesday 13:15 - 15:15 4.1 Matteo Matteucci Questions and Answers

(*) Linear regression methods (Ch 2.1, 2.2, 2.3 and 2.3.1, 2.4, 2.6 and 2.6.1, 2.7 and 2.7.1, 2.8 and 2.8.1, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.9, see paper "Least Angle Regression" linked below, pages 1-16)

Course Evaluation

The course evaluation is composed by two parts:

  • A homework with exercises covering the whole program that counts for 30% of the course grade
  • A written examination covering the whole program that count for 70% of the course grade

The homework is just one per year, it will be published at the end of the course and you will have 15 days to turn it in. It is not mandatory, however if you do not turn it in you loose 30% of the course grade. There is the option of substitute the homework with a practical project, but this has to be discussed and agreed with the course professor.

Teaching Material (the textbook)

Lectures will be based on material taken from the aforementioned slides and from the following book.

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:

  • Course introduction: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised learning and two algorithms for classification (taken from The Elements of Statistical Learning book).
  • Probability Basics: Slides on probability basics used to introduce Statistical Decision Theory.
  • Linear Classification Examples: slides presenting images, tables and examples about (generalized) linear methods for classification (taken from The Elements of Statistical Learning book).
  • Kernel Smoothing Examples: slides presenting images, tables and examples about Kernel Smoothing, Kernel Density Estimation and Gaussian Mixture Models (taken from The Elements of Statistical Learning book).
  • Decision Trees and Classification Rules: these slides have been used to present decision trees and decision rules complementing the material in Ch. 9.2 of the The Elements of Statistical Learning book.
  • Support Vector Machines: these slides have been used to present Support Vector Machines (taken from The Elements of Statistical Learning book).

Additional Papers

Papers used to integrate the textbook

Clustering Slides

These are the slides used to present clustering algorithms during lectures

  • Lesson 3: Mixture of Gaussians, DBSCAN, Jarvis-Patrick (slides, handouts)

Past Exams and Sample Questions

These are the text of past exams to give and idea on what to expect during the class exam:

2013 Homework

TBA


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