Difference between revisions of "Pattern Analysis and Machine Intelligence"

Revision as of 01:52, 21 November 2014

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

14/11/2014: Updated the linear regression slides, and the detailed schedule
09/11/2014: Change in the detailed schedule, Matteucci will be teaching instead of Eynard 
07/11/2014: Update of the full schedule for the course + the slides about regression + labs 1 to 3 material
26/10/2014: Tomorrow 27/10/2014 PAMI will start at 15:15 due to a change in the schedule with IRDM
10/10/2014: The new course edition is about to start!!! Stay tuned it will be significantly different from the past (check the 'new words in the text)!!!!

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.

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
• Davide Eynard: the teaching assistant

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 following new book which is available for download in pdf

• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

A tentative syllabus for this year edition is the following (a detailed schedule of the lectures follows)

• 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!!

Note: Lecture timetable interpretation
* On Mondays, in V.08, starts at 13:15 (SHARP!), ends at 15:00
* On Fridays, in V.08, starts at 10:30 (quarto d'ora accademico), ends at 12:15 or 13:15

Course Evaluation

The new 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

Teaching Material (the textbook)

Lectures will be based on material taken from the book.

• An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

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

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction. by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

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:

• 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.
• Statistical Learning Introduction: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)
• 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.
• Linear Regression: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.

Exercising and Laboratories:

• Lab 1: Introduction to R
  • Basics of R: the list of commands ran in Lab 01. Note that the list is heavily based on the Lab in Section 2.3 of the book (you can find the original here), but I preferred to integrate it with some additional hints from my personal experience and other sources such as this one)
  • Statistical Learning: the website of the Introduction to Statistical Learning book. In the Data Sets and Figures page you will also find links to download the Auto.data and Auto.cvs datasets we used during the Lab.
  • The Comprehensive R Archive Network: the place where you can download R and its packages (note that the book often refers to ISLR and MASS packages, it is good for you to install them soon)
• Lab2: Questions and exercises on Statistical Learning
• Lab3: First exercises on linear regression

Additional Resources

Papers and links useful to integrate the textbook

• Bias vs. Variance: "Understanding the Bias-Variance Tradeoff" essay by Scott Fortmann-Roe
• ...

Past Exams and Sample Questions

This edition of the course in new so the exams of this year will vary in the format

• 2 theoretical questions
• 2 practical exercises

These are the text of past exams to give and idea on what to expect a theoretical questions:

• 20/09/2013 Exam
• 10/09/2013 Exam
• 26/07/2013 Exam
• 11/07/2013 Exam
• 29/01/2013 Exam
• 19/09/2012 Exam
• 04/09/2012 Exam
• 10/07/2012 Exam
• 26/06/2012 Exam
• 03/02/2012 Exam
• 19/09/2011 Exam
• 08/09/2011 Exam
• 15/07/2011 Exam
• 29/06/2011 Exam

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)