Difference between revisions of "Pattern Analysis and Machine Intelligence"

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The following are last minute news you should be aware of ;-)
 
The following are last minute news you should be aware of ;-)
 +
09/10/2016: Scores from the 28/09/2016 written exam are [[Media:Grades_160928_PAMI.pdf|published here]]!!
 +
20/09/2016: Scores from the 09/09/2016 written exam are [[Media:Grades_160909_PAMI.pdf|published here]]!!
 +
31/07/2016: Scores from the 06/07/2016 written exam are [[Media:Grades_160706_PAMI.pdf|published here]]!!
 +
13/03/2016: Scores from the 19/02/2016 written exam are [[Media:Grades_160219_PAMI.pdf|published here]]!!
 +
16/02/2016: Scores from the 03/02/2016 written exam are [[Media:Grades_160203_PAMI.pdf|published here]]!!
 +
18/01/2015: PAMI Homework has been published!
 +
15/12/2015: Schedule revised until January (Note: on Friday 18/12/2015 there will be exercising with Eynard)
 +
09/12/2015: PAMI Exams for the Winter Calls will be on: 03/02/2016 and 19/02/2016
 +
25/10/2015: Updated slides on Statistical Decision Theory and Model Assessment
 +
11/10/2015: Added link to Teaching Assistant website for his material
 +
09/10/2015: New edition of PAMI website is out, stay tuned!
 +
<!--
 +
15/07/2015: The [[Media:Grades_150706_PAMI.pdf |grades from the 06/07/2015 call]] are out.
 +
15/02/2015: The [[Media:Grades_150209_PAMI.pdf |grades from the 09/02/2015 call]] are out.
 +
09/01/2015: The 2015 homework is out!!
 +
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)!!!!
 +
23/09/2014: Scores from the 15/09/2014 written exam including the Homeworks are [[Media:Grades_140915_PAMI.pdf|published here]]!!
 +
24/07/2014: Scores from the 30/06/2014 written exam including the Homeworks are [[Media:Grades_140630_PAMI.pdf|published here]]!!
 +
21/03/2014: Scores from the 20/02/2014 written exam including the Homeworks are [[Media:Grades_140220_PAMI.pdf|published here]]!!
 +
18/02/2014: Scores from the 06/02/2014 written exam including the Homeworks are [[Media:Grades_140206_PAMI_HW.pdf|published here]]!!
 +
17/02/2014: Scores from the 06/02/2014 written exam are [[Media:Grades_140206_PAMI.pdf|published here]] ... in hours you will get homeworks as well!!
 
  04/01/2014: The third homework for the 2013-2014 course edition has been published!
 
  04/01/2014: The third homework for the 2013-2014 course edition has been published!
 
  01/01/2014: Happy new year!!!
 
  01/01/2014: Happy new year!!!
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  08/10/2013: final grades for the [[Media:Grades_130920_PAMI.pdf|20/09/2013 exam]]  
 
  08/10/2013: final grades for the [[Media:Grades_130920_PAMI.pdf|20/09/2013 exam]]  
 
  07/10/2013: a new edition of the course starts today!
 
  07/10/2013: a new edition of the course starts today!
−
<!--
 
 
  21/08/2013: final grades for the [[Media:Grades_130726_PAMI.pdf|26/07/2013 exam]] (one grade is incomplete for technical reasons, it will be fixed soon)
 
  21/08/2013: final grades for the [[Media:Grades_130726_PAMI.pdf|26/07/2013 exam]] (one grade is incomplete for technical reasons, it will be fixed soon)
 
             final grades for the [[Media:Grades_Homeworks_130726_PAMI.pdf|homeworks at the date of 26/07/2013]]
 
             final grades for the [[Media:Grades_Homeworks_130726_PAMI.pdf|homeworks at the date of 26/07/2013]]
Line 49: Line 73:
 
==Course Aim & Organization==
 
==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.
+
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===
 
===Teachers===
  
−
The course is composed by a blending of lectures and exercises by the course teacher and some teaching assistants.
+
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
  
 
* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the course teacher
 
* [http://www.dei.polimi.it/people/matteucci Matteo Matteucci]: the course teacher
−
* [http://www.dei.polimi.it/people/malago Luigi Malago']: the teaching assistant on regression
+
* [http://davide.eynard.it/ Davide Eynard]: the teaching assistant
−
* [http://davide.eynard.it/ Davide Eynard]: the teaching assistant on clustering
+
  
 
===Course Program===
 
===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):
+
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
  
−
* '''''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;  
+
* [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
−
* '''''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.
+
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:
−
* '''''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. ).
+
* '''''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===
 
===Detailed course schedule===
Line 74: Line 101:
  
 
  Note: Lecture timetable interpretation
 
  Note: Lecture timetable interpretation
−
  * On Mondays lectures start at 13:15 SHARP
+
  * On Mondays, in V.08, starts at 13:30 (quarto d'ora accademico), ends at 15:15
−
  * On Fridays lectures start at 10:30 (quarto d'ora accademico)
+
  * On Fridays, in V.08, starts at 10:30 (quarto d'ora accademico), ends at 12:15 or 13:15 (check!)
  
 
{| border="1" align="center" style="text-align:center;"
 
{| border="1" align="center" style="text-align:center;"
Line 81: Line 108:
 
|Date || Day || Time || Room || Teacher || Topic
 
|Date || Day || Time || Room || Teacher || Topic
 
|-
 
|-
−
|07/10/2013 || Monday || 13:15 - 15:00 || V08 || Matteo Matteucci || Course Introduction (Ch. 1)
+
|05/10/2015 || Monday || 13:15 - 15:15 || V08 || Matteo Matteucci || Course Introduction (Ch. 1 ISL)
 
|-
 
|-
−
|11/10/2013 || Friday || 10:30 - 13:15 || VS9 || Matteo Matteucci || Two examples from classification + probability review(Ch. 2)
+
|09/10/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
 
|-
 
|-
−
|14/10/2013 || Monday || 13:15 - 15:00 || VS9 || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. The Bayes classifier (Ch. 2)
+
|12/10/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Introduction to R (Ch. 2 ISL)
 
|-
 
|-
−
|18/10/2013 || Friday || 10:30 - 13:15 || VS9 || Matteo Matteucci || Discriminating functions, decision boundary (Ch.4.1, Ch. 4.2)
+
|16/10/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
 
|-
 
|-
−
|21/10/2013 || Monday || 13:15 - 15:00 || VS9 || Matteo Matteucci || Linear Regression on the Indicator Matrix (Ch. 4.3)
+
|19/10/2015 || Monday || - || - || - || No PAMI Classes Today
 
|-
 
|-
−
|25/10/2013 || Friday || 10:30 - 13:15 || VS9 || Matteo Matteucci || Linear Discriminant Analysis (Ch. 4.3)
+
|23/10/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
 
|-
 
|-
−
|28/10/2013 || Monday || 13:15 - 15:00 || V08 || Matteo Matteucci || Regularized Linear Discriminant Analysis, LDA in the (K-1) subspace (Ch. 4.3)
+
|26/10/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Statistical Decision Theory Exercises (Ch. 2 ISL)
 
|-
 
|-
−
|04/11/2013 || Monday || 13:15 - 15:15 || V08 || Matteo Matteucci || Fisher Projection - Logistic Regression (Ch. 4.4)
+
|30/11/2015 || Friday || 10:15 - 12:15 || V.S8-B || Matteo Matteucci || Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
 
|-
 
|-
−
|08/11/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Logistic Regression (Ch. 4.4)
+
|02/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Simple Linear Regression (Ch. 3 ISL)
 
|-
 
|-
−
|11/11/2013 || Monday || 13:15 - 15:00 || V08 || Luigi Malagò    || Linear Regression Methods (Ch. 2, Ch. 3, (*))
+
|06/11/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
 
|-
 
|-
−
|15/11/2013 || Friday || 10:30 - 13:15 || V08 || Luigi Malagò    || Linear Regression Methods (Ch. 2, Ch. 3, (*))
+
|09/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Linear Regression and Feature Selection
 
|-
 
|-
−
|18/11/2013 || Monday || 13:15 - 15:00 || V08 || Luigi Malagò    || Linear Regression Methods (Ch. 2, Ch. 3, (*))
+
|13/11/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
 
|-
 
|-
−
|22/11/2013 || Friday || 10:30 - 13:15 || 4.1 || Luigi Malagò    || Linear Regression Methods (Ch. 2, Ch. 3, (*))
+
|16/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Intro Clustering
 
|-
 
|-
−
|25/11/2013 || Monday || 13:15 - 15:00 || V08 || Davide Eynard   || Clustering I: Introduction and K-Means
+
|20/11/2015 || Friday || 10:15 - 12:15 || V.S8-B || Davide Eynard || Clustering with exercises
 
|-
 
|-
−
|29/11/2013 || Friday || 10:30 - 13:15 || V08 || Davide Eynard   ||   Clustering II: K-Means Alternatives, Hierarchical, SOM
+
|23/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Clustering Advanced
 
|-
 
|-
−
|02/12/2013 || Monday || 13:15 - 15:00 || V08 || Davide Eynard    ||   Clustering III: Mixture of Gaussians, DBSCAN, Jarvis-Patrick
+
|27/11/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
 
|-
 
|-
−
|06/12/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Perceptron Learning and Maximum Margin Classifiers (Ch.4.5.2)
+
|30/11/2015 || Monday || 13:15 - 15:15 || V.S8-B || Davide Eynard || Exercises on Clustering
 
|-
 
|-
−
|13/12/2013 || Friday || 10:30 - 13:15 || V08 || Davide Eynard    ||   Clustering IV: Spectral Clustering and Evaluation Measures
+
|04/12/2015 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
 
|-
 
|-
−
|16/12/2013 || Monday || 13:15 - 15:00 || V08 || Matteo Matteucci || Support Vector Marchines (Ch.12.1, 12.2, 12.3)
+
|07/12/2015 || Monday || - || - || - || No PAMI Classes Today
 
|-
 
|-
−
|20/12/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Kernel Smoothing (Ch.6.1) and Kernel Density Estimation (Ch.6.6, Ch.6.9)
+
|11/12/2014 || Friday || 10:15 - 13:15 || V.S8-B || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
 +
|-
 +
|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 || Davide Eynard || Exercises on Classification
 +
|-
 +
|21/12/2015 || Monday ||  -  || - || - || No PAMI Classes Today
 +
|-
 +
|11/01/2014 || Monday || 13:15 - 15:15 || V.S8-B || Matteo Matteucci || Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
 +
|-
 +
|15/12/2015 || Friday || 10:15 - 12:15 || 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
 +
 
 +
<!---
 +
|13/10/2014 || Monday || 15:15 - 18:15 || V08 || Matteo Matteucci || Course Introduction (Ch. 1 ISL)
 +
|17/10/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
 +
|20/10/2014 || Monday || 13:15 - 15:15 || V08 || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
 +
|24/10/2014 || Friday || 10:30 - 12:15 || V08 || Davide Eynard || Introduction to R (Ch. 2 ISL)
 +
|27/10/2014 || Monday || 15:15 - 17:15 || V08 || Davide Eynard || Statistical Decision Theory Exercises (Ch. 2 ISL)
 +
|31/10/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Simple Linear Regression (Ch. 2 ISL)
 +
|03/11/2014 || Monday || 13:30 - 15:15 || V08 || Davide Eynard || Exercises on Simple Linear Regression (Ch. 3 ISL)
 +
|07/11/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Linear Regression (Ch. 3 ISL)
 +
|10/11/2014 || Monday || 13:30 - 15:15 || V08 || Matteo Matteucci || Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
 +
|14/11/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
 +
|17/11/2014 || Monday || 13:30 - 15:15 || V08 || Davide Eynard || Exercises on Linear Regression and Feature Selection
 +
|21/11/2014 || Friday || 10:30 - 12:15 || V08 || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
 +
|24/11/2014 || Monday ||  -  || - || - || No classes this week
 +
|28/11/2014 || Friday ||  -  || - || - || No classes this week
 +
|01/12/2014 || Monday || 13:30 - 15:15 || V08 || Davide Eynard || Exercises on Classification
 +
|05/12/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
 +
|12/12/2014 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
 +
|15/12/2014 || Monday || 13:10 - 15:00 || V08 || Davide Eynard || Exercises on Classification
 +
| -  || - || - || - || - || Holidays
 +
|09/01/2015 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
 +
|12/01/2015 || Monday || 13:10 - 15:00 || V08 || Davide Eynard || Intro Clustering
 +
|16/01/2015 || Friday || 10:30 - 12:15 || V08 || Davide Eynard || Clustering with exercises
 +
|19/01/2015 || Monday || 13:10 - 15:00 || V08 || Davide Eynard || Clustering Advanced
 +
|23/01/2015 || Friday || 10:30 - 12:15 || V08 || Davide Eynard || Exercises on Clustering
 +
 
 +
|21/10/2013 || Monday || 13:15 - 15:00 || VS9 || Matteo Matteucci || Linear Regression on the Indicator Matrix (Ch. 4.3)
 +
|25/10/2013 || Friday || 10:30 - 13:15 || VS9 || Matteo Matteucci || Linear Discriminant Analysis (Ch. 4.3)
 +
|28/10/2013 || Monday || 13:15 - 15:00 || V08 || Matteo Matteucci || Regularized Linear Discriminant Analysis, LDA in the (K-1) subspace (Ch. 4.3)
 +
|04/11/2013 || Monday || 13:15 - 15:15 || V08 || Matteo Matteucci || Fisher Projection - Logistic Regression (Ch. 4.4)
 +
|08/11/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Logistic Regression (Ch. 4.4)
 +
|11/11/2013 || Monday || 13:15 - 15:00 || V08 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 +
|15/11/2013 || Friday || 10:30 - 13:15 || V08 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 +
|18/11/2013 || Monday || 13:15 - 15:00 || V08 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 +
|22/11/2013 || Friday || 10:30 - 13:15 || 4.1 || Luigi Malagò    ||  Linear Regression Methods (Ch. 2, Ch. 3, (*))
 +
|25/11/2013 || Monday || 13:15 - 15:00 || V08 || Davide Eynard    ||  Clustering I: Introduction and K-Means
 +
|29/11/2013 || Friday || 10:30 - 13:15 || V08 || Davide Eynard    ||  Clustering II: K-Means Alternatives, Hierarchical, SOM
 +
|02/12/2013 || Monday || 13:15 - 15:00 || V08 || Davide Eynard    ||  Clustering III: Mixture of Gaussians, DBSCAN, Jarvis-Patrick
 +
|06/12/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Perceptron Learning and Maximum Margin Classifiers (Ch.4.5.2)
 +
|13/12/2013 || Friday || 10:30 - 13:15 || V08 || Davide Eynard    ||  Clustering IV: Spectral Clustering and Evaluation Measures
 +
|16/12/2013 || Monday || 13:15 - 15:00 || V08 || Matteo Matteucci || Support Vector Marchines (Ch.12.1, 12.2, 12.3)
 +
|20/12/2013 || Friday || 10:30 - 13:15 || V08 || Matteo Matteucci || Kernel Smoothing (Ch.6.1) and Kernel Density Estimation (Ch.6.6, Ch.6.9)
 +
 
 
Kernel Smoothing Methods and Kerned Density Estimation (Ch.6.1,
 
Kernel Smoothing Methods and Kerned Density Estimation (Ch.6.1,
 
Gaussian Mixture Models (Ch.6.8) and the EM Algorithm (Ch.8.5)
 
Gaussian Mixture Models (Ch.6.8) and the EM Algorithm (Ch.8.5)
Line 132: Line 216:
 
Model Selection (Ch. 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.10,)
 
Model Selection (Ch. 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.10,)
 
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)
 
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)
−
-->
 
  
 
(*) With respect to following version of the book [http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf PDF file of book (10th printing with corrections, Jan 2013)]
 
(*) With respect to following version of the book [http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf PDF file of book (10th printing with corrections, Jan 2013)]
Line 154: Line 237:
 
**3.9 take a look
 
**3.9 take a look
 
*As to the paper [http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Least Angle Regression] take a look at section 1 to 3
 
*As to the paper [http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Least Angle Regression] take a look at section 1 to 3
 +
-->
  
 
===Course Evaluation===
 
===Course Evaluation===
  
−
The course evaluation is composed by two parts:
+
The '''new''' course evaluation is composed by two parts:
  
−
* A homework with exercises covering the whole program that counts for 30% of the course grade
+
* HW: Homework with exercises covering the whole program
−
* A written examination covering the whole program that count for 70% of the course grade
+
* WE: A written examination covering the whole program
  
−
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 repeat the homework after the end of the course, but this has to be discussed and agreed with the course professor.
+
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====
 +
 
 +
We have published the [http://davide.eynard.it/2016/01/11/statistical-learning-with-r-2016-edition/ Homework 2015/2016]. 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
 +
 
 +
====Homework 2014/2015====
 +
 
 +
We have published the [http://davide.eynard.it/2015/01/05/statistical-learning-with-r-introduction-and-setup/ 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)==  
 
==Teaching Material (the textbook)==  
  
−
Lectures will be based on material taken from the aforementioned slides and from the following book.
+
Lectures will be based on material taken from the book.
 +
 
 +
* [http://www-bcf.usc.edu/~gareth/ISL/ 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
  
 
* [http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html The Elements of Statistical Learning: Data Mining, Inference, and Prediction.] by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
 
* [http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html The Elements of Statistical Learning: Data Mining, Inference, and Prediction.] by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
Line 174: Line 284:
 
===Teacher Slides===
 
===Teacher Slides===
  
−
In the following you can find the lecture slides used by the teacher and the teaching assistants during classes:
+
In the following you can find the lecture slides used by the teacher and the teaching assistants during classes.
  
 +
Lectures:
 +
* [[Media:PAMI2015-01-Intro.pdf | [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.
 +
* [[Media:PAMI2015-02-StatisticalLearning.pdf | [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.)
 +
* [[Media:PAMI2015-03-AssessingModelAccuracy.pdf | [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.
 +
* [[Media:PAMI2014-04-LinearRegression.pdf | [2014-2015] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.
 +
* [[Media:PAMI2014-05-LinearClassification.pdf | [2014-2015] Linear Classification]]: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods.
 +
* [[Media:PAMI2014-06-SupportVectorMachines.pdf | [2014-2015] 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 [http://davide.eynard.it/pattern-analysis-and-machine-intelligence-2015-2016/ Davide Eynard website].
 +
 +
<!--
 +
http://davide.eynard.it/pattern-analysis-and-machine-intelligence-2015-2016/
 +
* Lab 1: Introduction to R
 +
**[[Media:BasicsofR.txt | 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 [http://www-bcf.usc.edu/~gareth/ISL/code.html here]), but I preferred to integrate it with some additional hints from my personal experience and other sources such as [http://www.pitt.edu/~njc23/ this one])
 +
**[http://www.statlearning.com/ Statistical Learning]: the website of the Introduction to Statistical Learning book. In the [http://www-bcf.usc.edu/~gareth/ISL/data.html Data Sets and Figures] page you will also find links to download the Auto.data and Auto.cvs datasets we used during the Lab.
 +
**[http://cran.r-project.org/ 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)
 +
* [[Media:Lab02.pdf | Lab2]]: Questions and exercises on Statistical Learning
 +
* [[Media:Lab03.pdf | Lab3]]: First exercises on linear regression
 +
-->
 +
<!--
 
* [[Media:PAMI_Intro.pdf | 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).
 
* [[Media:PAMI_Intro.pdf | 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).
 
* [[Media:ProbabilityBasics.pdf | Probability Basics]]: Slides on probability basics used to introduce Statistical Decision Theory.
 
* [[Media:ProbabilityBasics.pdf | Probability Basics]]: Slides on probability basics used to introduce Statistical Decision Theory.
Line 181: Line 311:
 
* [[Media:PAMI_LinearClassification.pdf | Linear Classification Examples]]: slides presenting images, tables and examples about (generalized) linear methods for classification (taken from ''The Elements of Statistical Learning'' book).
 
* [[Media:PAMI_LinearClassification.pdf | Linear Classification Examples]]: slides presenting images, tables and examples about (generalized) linear methods for classification (taken from ''The Elements of Statistical Learning'' book).
 
* [[Media:PAMI_KernelSmoothing.pdf | 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).
 
* [[Media:PAMI_KernelSmoothing.pdf | 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).
−
<!--* [[Media:PAMI_DTnR.pdf | 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.-->
+
* [[Media:PAMI_DTnR.pdf | 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.
 
* [[Media:PAMI_SVM.pdf | Support Vector Machines]]: these slides have been used to present Support Vector Machines (taken from ''The Elements of Statistical Learning'' book).
 
* [[Media:PAMI_SVM.pdf | Support Vector Machines]]: these slides have been used to present Support Vector Machines (taken from ''The Elements of Statistical Learning'' book).
 +
-->
  
−
===Additional Papers===
+
===Additional Resources===
−
Papers used to integrate the textbook
+
Papers and links useful to integrate the textbook
  
 +
* [http://scott.fortmann-roe.com/docs/BiasVariance.html Bias vs. Variance]: "Understanding the Bias-Variance Tradeoff" essay by Scott Fortmann-Roe
 +
* ...
 +
<!--
 
* Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, [http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Least Angle Regression] Annals of Statistics (with discussion) (2004) 32(2), 407-499.
 
* Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, [http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Least Angle Regression] Annals of Statistics (with discussion) (2004) 32(2), 407-499.
 
* Burges, Christopher J. C., 1998. [http://www.svms.org/tutorials/Burges1998.pdf A tutorial on support vector machines for pattern recognition]. Data Mining and Knowledge Discovery, 2(2), 121–167.
 
* Burges, Christopher J. C., 1998. [http://www.svms.org/tutorials/Burges1998.pdf A tutorial on support vector machines for pattern recognition]. Data Mining and Knowledge Discovery, 2(2), 121–167.
 
* ...  
 
* ...  
 +
-->
  
 +
<!--
 
===Clustering Slides===
 
===Clustering Slides===
 
These are the slides used to present clustering algorithms during lectures
 
These are the slides used to present clustering algorithms during lectures
Line 201: Line 337:
  
 
* Lesson 4: Evaluation measures ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e4.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e4.pdf handouts]) and Spectral Clustering ([http://davide.eynard.it/teaching/2012_PAMI/Spectral%20Clustering.pdf])
 
* Lesson 4: Evaluation measures ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e4.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e4.pdf handouts]) and Spectral Clustering ([http://davide.eynard.it/teaching/2012_PAMI/Spectral%20Clustering.pdf])
 +
 +
-->
  
 
===Past Exams and Sample Questions===
 
===Past Exams and Sample Questions===
−
These are the text of past exams to give and idea on what to expect during the class exam:
+
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:
 +
 
 +
* [[Media:2015_02_09_PAMI.pdf |09/02/2015 Exam]]
 +
* [[Media:2015_02_23_PAMI.pdf |23/02/2015 Exam]]
 +
* [[Media:2015_06_07_PAMI.pdf |07/06/2015 Exam]]
 +
* [[Media:2015_09_14_PAMI.pdf |14/09/2015 Exam]]
 +
* [[Media:2015_09_30_PAMI.pdf |30/09/2015 Exam]]
 +
 
 +
These are the text of past exams to give and idea on what to expect a theoretical questions:
  
 
* [[Media:2013_09_20_PAMI.pdf |20/09/2013 Exam]]
 
* [[Media:2013_09_20_PAMI.pdf |20/09/2013 Exam]]
Line 226: Line 372:
 
* [http://math.arizona.edu/~hzhang/math574m.html 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'')
 
* [http://math.arizona.edu/~hzhang/math574m.html 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'')
  
 +
<!--
 
== 2013-2014 Homework ==
 
== 2013-2014 Homework ==
  
Line 262: Line 409:
 
* [http://davide.eynard.it/2013/12/30/octave-clustering-demo-part-6-more-evaluation/ Homework 2013-2014 on clustering evaluation]
 
* [http://davide.eynard.it/2013/12/30/octave-clustering-demo-part-6-more-evaluation/ Homework 2013-2014 on clustering evaluation]
  
−
As usual, '''this part of the homework will contribute to the 10% of the grade'''; the deadline to submit the solution is the end of the course, , sending it to davide.eynard_at_gmail.com.
+
As usual, '''this part of the homework will contribute to the 10% of the grade'''; the deadline to submit the solution is the end of the course. You have to '''send it to davide.eynard_at_gmail.com Friday 24/01 23:59'''.
 +
 
 +
'''Note 1:''' for any doubt or question send an email, as soon as possible, to Davide Eynard so to have a prompt reply and not get stuck during homework execution.
 +
 
 +
'''Note 2:''' you have to turn in only the solution of "Ocatave clustering demo part 6", while the other parts can be used as reference to improve your understanding in basic clustering algorithms.
  
−
<!--
 
 
=== Part 2: Classification ===
 
=== Part 2: Classification ===
  

Latest revision as of 02:01, 9 October 2016


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

09/10/2016: Scores from the 28/09/2016 written exam are published here!!
20/09/2016: Scores from the 09/09/2016 written exam are published here!!
31/07/2016: Scores from the 06/07/2016 written exam are published here!!
13/03/2016: Scores from the 19/02/2016 written exam are published here!!
16/02/2016: Scores from the 03/02/2016 written exam are published here!!
18/01/2015: PAMI Homework has been published!
15/12/2015: Schedule revised until January (Note: on Friday 18/12/2015 there will be exercising with Eynard)
09/12/2015: PAMI Exams for the Winter Calls will be on: 03/02/2016 and 19/02/2016
25/10/2015: Updated slides on Statistical Decision Theory and Model Assessment
11/10/2015: Added link to Teaching Assistant website for his material
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 PAMI Classes Today
23/10/2015 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Statistical Decision Theory and Model Assessment. (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. 2 ISL + 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 (Ch. 2 ISL + Ch. 3 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 Linear Regression and Feature Selection (Ch. 3 + 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 Feature Selection and Shrinkage in Linear Regression (Ch. 6 ISL)
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 Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
07/12/2015 Monday - - - No PAMI Classes Today
11/12/2014 Friday 10:15 - 13:15 V.S8-B Matteo Matteucci Classification by Linear Discriminant Analysis (Ch. 4 ISL)
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 Davide Eynard Exercises on Classification
21/12/2015 Monday - - - No PAMI Classes Today
11/01/2014 Monday 13:15 - 15:15 V.S8-B Matteo Matteucci Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
15/12/2015 Friday 10:15 - 12:15 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 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

We have published the Homework 2015/2016. 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

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.
  • [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.)
  • [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.

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
  • ...


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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