Information Retrieval and Data Mining

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

09/10/2015: New edition of PAMI website is out, stay tuned!

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.

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

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 Classification Techniques: linear methods for classification will be presented as the starting point (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)
  • Linear Regression Techniques: linear methods for regression will be disccussed and compared (e.g., Linear Regression, Ridge Regression, Lasso, LARS).
  • 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 (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 13:30 (quarto d'ora accademico), ends at 15:15
* On Fridays, in V.08, starts at 10:30 (quarto d'ora accademico), ends at 12:15 or 13:15 (check!)
Date Day Time Room Teacher Topic
05/10/2015 Monday 13:15 - 15:15 V08 Matteo Matteucci Course Introduction (Ch. 1 ISL)
09/10/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
12/10/2015 Monday 13:15 - 15:15 V.S8-B Davide Eynard Introduction to R (Ch. 2 ISL)
16/10/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
19/10/2015 Monday - - - No classes this week
23/10/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Simple Linear Regression (Ch. 2 ISL)
26/10/2015 Monday 13:15 - 15:15 V.S8-B Davide Eynard Statistical Decision Theory Exercises (Ch. 2 ISL)
30/11/2015 Friday 10:15 - 12:15 V.S8-B Matteo Matteucci Linear Regression (Ch. 3 ISL)
02/11/2015 Monday 13:15 - 15:15 V.S8-B Davide Eynard Exercises on Simple Linear Regression (Ch. 3 ISL)
06/11/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
09/11/2015 Monday 13:15 - 15:15 V.S8-B Davide Eynard Exercises on Linear Regression and Feature Selection
13/11/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
16/11/2015 Monday 13:15 - 15:15 V.S8-B Davide Eynard Intro Clustering
20/11/2015 Friday 10:15 - 12:15 V.S8-B Davide Eynard Clustering with exercises
23/11/2015 Monday 13:15 - 15:15 V.S8-B Davide Eynard Clustering Advanced
27/11/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
30/11/2015 Monday 13:15 - 15:15 V.S8-B Davide Eynard Exercises on Clustering
04/12/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Classification by Linear Discriminant Analysis (Ch. 4 ISL)
07/12/2015 Monday - - - No classes this week
11/12/2014 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
14/12/2014 Monday 13:15 - 15:15 V.S8-B Davide Eynard Exercises on Classification
18/12/2015 Friday 10:15 - 12:15 V.S8-B Matteo Matteucci Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
21/12/2014 Monday 13:15 - 15:15 V.S8-B Davide Eynard Exercises on Classification

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 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

Homeworks

Homework 2015/2016

Not published yet ...

Homework 2014/2015

We have published the Homework 2014/2015. 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
  • the deadline to turn the homework in is the first time you take the PAMI exam, we will grade it when grading your classwork
  • you can make the homework in groups up to 2/3 people, the deadline to turn it in is the date the first person in the groups takes the exam (and it will be graded for all members in the group at that call)
  • 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

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:

  • [2015] 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.
  • [2014-2015] Statistical Learning Introduction: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)
  • [2014-2015] 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.
  • [2014-2015] Linear Regression: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.
  • [2014-2015] Linear Classification: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods.
  • [2014-2015] Support Vector Machines: Discriminative vs. generative methids. Hyperplanes learning and Perceptron. Maximum Margin Classifiers. The Kernel trick and Support Vector Machines.


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

Since 2014/2015 the course was changed and the exams format as well. For this edition of the course you should expect 2 theoretical questions + 2 practical exercises (on average). Some examples from the past year can be found here:

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

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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