Difference between revisions of "Machine Learning"

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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 ;-)
 +
* 03/02/2019: Homework published!!!
 +
* 09/12/2018: Added a new lecture on Friday 14/12 from 10:30 till 13:30 and updated the course program
 +
* 22/10/2018: Modified detailed schedule
 +
* 17/10/2018: New edition of the course starts today!!!
 +
* 08/08/2018: Detailed [[Media:Grades_180710_ML.pdf|grades for the 10/07/2018 exam]] are here
 +
* 11/03/2018: Detailed [[Media:Grades_180209_ML.pdf|grades for the 09/02/2018 exam]] are here
 +
* 06/02/2018: Detailed [[Media:Grades_180119_ML.pdf|grades for the 19/01/2018 exam]] are here
 +
* 05/01/2017: 2017/2018 published, deadline 21st of January.
 +
* 14/12/2017: An extra lecture is added on 19/12/2017 @ 10:30 to recover the one canceled on the 28/11/2017
 +
* 27/11/2017: The 28/11/2017 lecture is canceled due to teacher sickness
 +
* 19/09/2017: Updated material from the first two lectures
 +
* 18/09/2017: The course starts today!
 +
 +
<!--
 
  * 09/09/2017: Detailed [[Media:Grades_170829_ML.pdf|grades for the 29/08/2017 exam]] are here
 
  * 09/09/2017: Detailed [[Media:Grades_170829_ML.pdf|grades for the 29/08/2017 exam]] are here
 
  * 02/08/2017: Detailed [[Media:Grades_170725_ML.pdf|grades for the 25/07/2017 exam]] are here
 
  * 02/08/2017: Detailed [[Media:Grades_170725_ML.pdf|grades for the 25/07/2017 exam]] are here
Line 14: Line 28:
 
  * 04/10/2016: Lectures start today!
 
  * 04/10/2016: Lectures start today!
 
  * 01/09/2016: This is the Machine Learning course website. The course is basically a rename of the [[Pattern_Analysis_and_Machine_Intelligence|PAMI]] course.
 
  * 01/09/2016: This is the Machine Learning course website. The course is basically a rename of the [[Pattern_Analysis_and_Machine_Intelligence|PAMI]] course.
 +
-->
 
==Course Aim & Organization==
 
==Course Aim & Organization==
  
Line 44: Line 59:
  
 
  Note: Lecture timetable interpretation
 
  Note: Lecture timetable interpretation
−
  * On Tuesday, in room V.S8-B, starts at 8:30 (quarto d'ora accademico), ends at 10:15
+
  * On Monday, in room V.S7-A, starts at 08:15 (cum tempore), ends at 10:15
−
  * On Fridays, in room V.S8-A, starts at 10:30 (quarto d'ora accademico), ends at 12:15 or 13:15 (check!)
+
  * On Tuesday, in room V.S7-A, starts at 09:15 (cum tempore), ends at 12:15
  
 
{| border="1" align="center" style="text-align:center;"
 
{| border="1" align="center" style="text-align:center;"
Line 51: Line 66:
 
|Date || Day || Time || Room || Teacher || Topic
 
|Date || Day || Time || Room || Teacher || Topic
 
|-
 
|-
−
|04/10/2016 || Tuesday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Course Introduction (Ch. 1 ISL)
+
|17/09/2017 || Monday  || 08:15 - 10:15 || V.S7-A || 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)
+
|18/09/2017 || Tuesday || --- || --- || --- || No Lecture
 
|-
 
|-
−
|11/10/2016 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard    || Intro Clustering
+
|24/09/2017 || Monday  || 08:15 - 10:15 || V.S7-A || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
 
|-
 
|-
−
|14/10/2016 || Friday  || 10:15 - 12:15 || V.S8-A || Davide Eynard    || Clustering with exercises
+
|25/09/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
 
|-
 
|-
−
|18/10/2016 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard   || Clustering Advanced
+
|01/10/2017 || Monday  || 08:15 - 10:15 || V.S7-A || Davide Eynard || Introduction to R (Ch. 2 ISL)
 
|-
 
|-
−
|21/10/2016 || Friday  || 10:15 - 12:15 || V.S8-A || Davide Eynard   || Exercises on Clustering
+
|02/10/2017 || Tuesday || 09:15 - 11:15 || V.S7-A || Davide Eynard || Statistical Decision Theory Exercises (Ch. 2 ISL)
 
|-
 
|-
−
|25/10/2016 || Tuesday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
+
|08/10/2017 || Monday  || 08:15 - 10:15 || V.S7-A || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (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)
+
|09/10/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Matteo Matteucci || Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
 
|-
 
|-
−
|04/11/2016 || Friday || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
+
|15/10/2017 || Monday || 08:15 - 10:15 || V.S7-A || Davide Eynard || Exercises on Simple Linear Regression (Ch. 3 ISL)
 
|-
 
|-
−
|08/11/2016 || Tuesday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
+
|16/10/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || 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)
+
|22/10/2017 || Monday || 08:15 - 10:15 || V.S7-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)
+
|23/10/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Matteo Matteucci || Linear Regression and Feature Selection (Ch. 3 + Ch. 6 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)
+
|29/10/2017 || Monday  || 08:15 - 10:15 || V.S7-A || Matteo Matteucci || Linear Regression and Feature Selection (Ch. 3 + 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)
+
|30/10/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
 
|-
 
|-
−
|06/12/2016 || Tuesday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
+
|05/11/2017 || Monday  || --- || --- || --- || No Lecture (Suspension)
 
|-
 
|-
−
|13/12/2015 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard    || Exercises on Linear Regression and Feature Selection
+
|06/11/2017 || Tuesday || --- || --- || --- || No Lecture (Suspension)
 
|-
 
|-
−
|16/12/2016 || Friday || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
+
|12/11/2017 || Monday || 08:15 - 10:15 || V.S7-A || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
 
|-
 
|-
−
|20/12/2016 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard   || Exercises on Classification by Logistic Regression
+
|13/11/2017 || Tuesday || 09:15 - 11:15 || V.S7-A || Davide Eynard || Exercises on Linear Regression and Feature Selection
 
|-
 
|-
−
|23/12/2016 || Friday || 10:15 - 12:15 || V.S8-A || Davide Eynard   || Exercises on Classification by Linear Discriminant Analysis
+
|19/11/2017 || Monday || 08:15 - 10:15 || V.S7-A || Davide Eynard || Exercises on Classification by Logistic Regression
 
|-
 
|-
−
|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)
+
|20/11/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Davide Eynard || Exercises on Shrinkage methods
 
|-
 
|-
−
|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)
+
|26/11/2017 || Monday || 08:15 - 10:15 || V.S7-A || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
 
|-
 
|-
−
|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)
+
|27/11/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
 +
|-
 +
|03/12/2017 || Monday  || 08:15 - 10:15 || V.S7-A || Davide Eynard || Exercises on Classification by Linear Discriminant Analysis
 +
|-
 +
|04/12/2017 || Tuesday || --- || --- || --- || No Lecture
 +
|-
 +
|10/12/2017 || Monday  || 08:15 - 10:15 || V.S7-A || Davide Eynard || Clustering: Intro, k-means and the alike, GMM, and hierachical
 +
|-
 +
|11/12/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Davide Eynard || Clustering: Density-based, Spectral + Evaluation
 +
|-
 +
|14/12/2017 || Friday || 10:15 - 13:15 || TBD || Matteo Matteucci || Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
 +
|-
 +
|17/12/2017 || Monday  || 08:15 - 10:15 || V.S7-A || Matteo Matteucci || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
 +
|-
 +
|18/12/2017 || Tuesday || 09:15 - 12:15 || V.S7-A || Matteo Matteucci || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
 
|-
 
|-
 
|}
 
|}
Line 103: Line 130:
 
* Ch.12 ESL: Sections 12.1, 12.2, 12.3
 
* Ch.12 ESL: Sections 12.1, 12.2, 12.3
 
* Ch.9 ISL: Sections 9.1, 9.2, 9.3
 
* Ch.9 ISL: Sections 9.1, 9.2, 9.3
 +
 +
<!--
 +
{| border="1" align="center" style="text-align:center;"
 +
|-
 +
|Date || Day || Time || Room || Teacher || Topic
 +
|-
 +
|18/09/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Course Introduction (Ch. 1 ISL)
 +
|-
 +
|19/09/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Matteo Matteucci || Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
 +
|-
 +
|25/09/2017 || Monday  || 10:15 - 13:15 || V.S8-A || --- || No Lecture
 +
|-
 +
|26/09/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard    || Introduction to R (Ch. 2 ISL)
 +
|-
 +
|02/10/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
 +
|-
 +
|03/10/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Statistical Decision Theory Exercises (Ch. 2 ISL)
 +
|-
 +
|09/10/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
 +
|-
 +
|10/10/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Statistical Decision Theory Exercises (Ch. 2 ISL)
 +
|-
 +
|16/10/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
 +
|-
 +
|17/10/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Exercises on Simple Linear Regression (Ch. 3 ISL)
 +
|-
 +
|23/10/2017 || Monday  || 10:15 - 13:15 || V.S8-A || --- || No Lecture
 +
|-
 +
|24/10/2017 || Tuesday  || 08:15 - 10:15 || V.S8-B || --- || No Lecture
 +
|-
 +
|30/10/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
 +
|-
 +
|31/10/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Exercises on Simple Linear Regression (Ch. 3 ISL)
 +
|-
 +
|06/11/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
 +
|-
 +
|07/11/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Exercises on Linear Regression and Feature Selection
 +
|-
 +
|13/11/2017 || Monday  || 10:15 - 13:15 || V.S8-A || --- || No Lecture (Suspension)
 +
|-
 +
|14/11/2017 || Tuesday  || 08:15 - 10:15 || V.S8-B || --- || No Lecture (Suspension)
 +
|-
 +
|20/11/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
 +
|-
 +
|21/11/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Exercises on Classification by Logistic Regression
 +
|-
 +
|27/11/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Classification by Linear Discriminant Analysis (Ch. 4 ISL)
 +
|-
 +
|28/11/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Lecture canceled
 +
|-
 +
|04/12/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
 +
|-
 +
|05/12/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Exercises on Classification by Linear Discriminant Analysis
 +
|-
 +
|11/12/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
 +
|-
 +
|12/12/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Clustering: Intro, k-means and the alike
 +
|-
 +
|18/12/2017 || Monday  || 10:15 - 13:15 || V.S8-A || Matteo Matteucci || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
 +
|-
 +
|19/12/2017 || Tuesday || 08:15 - 10:15 || V.S8-B || Davide Eynard || Clustering: GMM, Hierarchical and Density-based
 +
|-
 +
|19/12/2017 || Tuesday || 10:15 - 12:15 || V.S8-B || Davide Eynard || Clustering: Spectral + Evaluation
 +
|-
 +
|}
 +
-->
  
 
===Course Evaluation===
 
===Course Evaluation===
Line 121: Line 214:
 
Please keep in mind:
 
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 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
 
* 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 28th of February 2019 we will grade it when grading your classwork
 +
* you can make the homework in groups up to 2/3 people
 +
 +
====Homework 2018/201====
 +
 +
We have published the [http://davide.eynard.it/2019/01/28/statistical-learning-with-r-2019-edition/ Homework 2018/2019].
 +
 +
====Homework 2017/2018====
 +
 +
We have published the [http://davide.eynard.it/2017/12/27/statistical-learning-with-r-2018-edition/ Homework 2017/2018].
  
 
====Homework 2016/2017====
 
====Homework 2016/2017====
Line 154: Line 255:
  
 
Lectures:
 
Lectures:
−
* [[Media:ML-2016-01-Intro.pdf | [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.
+
* [[Media:ML-2017-01-Intro.pdf | [2017] 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:ML-2016-02-StatisticalLearning.pdf | [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.)
+
* [[Media:ML-2017-02-StatisticalLearning.pdf | [2017] 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:ML-2016-03-AssessingModelAccuracy.pdf | [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.
 
* [[Media:ML-2016-03-AssessingModelAccuracy.pdf | [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.
 
* [[Media:ML-2016-04-LinearRegression.pdf | [2016] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.
 
* [[Media:ML-2016-04-LinearRegression.pdf | [2016] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.

Latest revision as of 23:28, 3 February 2019


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

* 03/02/2019: Homework published!!! 
* 09/12/2018: Added a new lecture on Friday 14/12 from 10:30 till 13:30 and updated the course program
* 22/10/2018: Modified detailed schedule
* 17/10/2018: New edition of the course starts today!!!
* 08/08/2018: Detailed grades for the 10/07/2018 exam are here
* 11/03/2018: Detailed grades for the 09/02/2018 exam are here
* 06/02/2018: Detailed grades for the 19/01/2018 exam are here
* 05/01/2017: 2017/2018 published, deadline 21st of January. 
* 14/12/2017: An extra lecture is added on 19/12/2017 @ 10:30 to recover the one canceled on the 28/11/2017 
* 27/11/2017: The 28/11/2017 lecture is canceled due to teacher sickness
* 19/09/2017: Updated material from the first two lectures
* 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.S7-A, starts at 08:15 (cum tempore), ends at 10:15
* On Tuesday, in room V.S7-A, starts at 09:15 (cum tempore), ends at 12:15
Date Day Time Room Teacher Topic
17/09/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Course Introduction (Ch. 1 ISL)
18/09/2017 Tuesday --- --- --- No Lecture
24/09/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Statistical Decision Theory and Bias-Variance trade off. (Ch. 2 ISL)
25/09/2017 Tuesday 09:15 - 12:15 V.S7-A Matteo Matteucci Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
01/10/2017 Monday 08:15 - 10:15 V.S7-A Davide Eynard Introduction to R (Ch. 2 ISL)
02/10/2017 Tuesday 09:15 - 11:15 V.S7-A Davide Eynard Statistical Decision Theory Exercises (Ch. 2 ISL)
08/10/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Statistical Decision Theory and Model Assessment. (Ch. 2 ISL)
09/10/2017 Tuesday 09:15 - 12:15 V.S7-A Matteo Matteucci Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
15/10/2017 Monday 08:15 - 10:15 V.S7-A Davide Eynard Exercises on Simple Linear Regression (Ch. 3 ISL)
16/10/2017 Tuesday 09:15 - 12:15 V.S7-A Matteo Matteucci Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
22/10/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
23/10/2017 Tuesday 09:15 - 12:15 V.S7-A Matteo Matteucci Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
29/10/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Linear Regression and Feature Selection (Ch. 3 + Ch. 6 ISL)
30/10/2017 Tuesday 09:15 - 12:15 V.S7-A Matteo Matteucci Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
05/11/2017 Monday --- --- --- No Lecture (Suspension)
06/11/2017 Tuesday --- --- --- No Lecture (Suspension)
12/11/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Classification by Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
13/11/2017 Tuesday 09:15 - 11:15 V.S7-A Davide Eynard Exercises on Linear Regression and Feature Selection
19/11/2017 Monday 08:15 - 10:15 V.S7-A Davide Eynard Exercises on Classification by Logistic Regression
20/11/2017 Tuesday 09:15 - 12:15 V.S7-A Davide Eynard Exercises on Shrinkage methods
26/11/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Classification by Linear Discriminant Analysis (Ch. 4 ISL)
27/11/2017 Tuesday 09:15 - 12:15 V.S7-A Matteo Matteucci Classification by Linear Discriminant Analysis (Ch. 4 ISL)
03/12/2017 Monday 08:15 - 10:15 V.S7-A Davide Eynard Exercises on Classification by Linear Discriminant Analysis
04/12/2017 Tuesday --- --- --- No Lecture
10/12/2017 Monday 08:15 - 10:15 V.S7-A Davide Eynard Clustering: Intro, k-means and the alike, GMM, and hierachical
11/12/2017 Tuesday 09:15 - 12:15 V.S7-A Davide Eynard Clustering: Density-based, Spectral + Evaluation
14/12/2017 Friday 10:15 - 13:15 TBD Matteo Matteucci Classification: from generative to discriminative approaches (Ch. 4 ISL + Ch. 4 ESL)
17/12/2017 Monday 08:15 - 10:15 V.S7-A Matteo Matteucci Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
18/12/2017 Tuesday 09:15 - 12:15 V.S7-A 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 28th of February 2019 we will grade it when grading your classwork
  • you can make the homework in groups up to 2/3 people

Homework 2018/201

We have published the Homework 2018/2019.

Homework 2017/2018

We have published the Homework 2017/2018.

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:

  • [2017] 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.
  • [2017] 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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