Difference between revisions of "Machine Learning Bio"

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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 ;-)
 +
* 24/02/2021: Lectures start today
 +
 +
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  * 08/08/2020: Grading for all projects and first two calls [[Media:Grades_20200723.pdf|are here!!]]  
 
  * 08/08/2020: Grading for all projects and first two calls [[Media:Grades_20200723.pdf|are here!!]]  
 
  * 22/06/2020: Grading mechanism explained, check [[Media:ML2020_grading_system.pdf|here a handy recap]]  
 
  * 22/06/2020: Grading mechanism explained, check [[Media:ML2020_grading_system.pdf|here a handy recap]]  
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  * 11/03/2020: The course starts today!
 
  * 11/03/2020: The course starts today!
 
  * 03/03/2020: The course is going to start soon ...
 
  * 03/03/2020: The course is going to start soon ...
 +
-->
  
 
==Course Aim & Organization==
 
==Course Aim & Organization==
  
−
The objective of the Machine Learning course is to give an in-depth presentation of the techniques most used for pattern recognition, knowledge discovery, and data analysis/modeling. These techniques are presented both from a theoretical (i.e., statistics and information theory) perspective and a practical one (i.e., coding examples) through the descriptions of algorithms and their implementations in a general purpose programming language.
+
The objective of the Machine Learning course is to give an in-depth presentation of the techniques most used for pattern recognition, knowledge discovery, and data analysis/modeling. These techniques are presented both from a theoretical (i.e., statistics and information theory) perspective and a practical one (i.e., coding examples) through the descriptions of algorithms and their implementations in a general-purpose programming language (i.e., python).
  
−
The course presents the classical supervised and unsupervised learning paradigms described and discussed presenting regression, classification, and clustering problems in Bioinformatics. The course is composed by a set of lectures on specific machine learning techniques (e.g., generalized linear regression, logistic regression, linear and quadratic discriminant analysis, support vector machines, k-nearest-neighborhood, clustering, etc.) preceded by the introduction of the Statistical Learning framework which acts as a common reference framework for the entire course.
+
The course presents the classical supervised and unsupervised learning paradigms described and discussed presenting regression, classification, and clustering problems in Bioinformatics. The course is composed of a set of lectures on specific machine learning techniques (e.g., generalized linear regression, logistic regression, linear and quadratic discriminant analysis, support vector machines, k-nearest-neighborhood, clustering, etc.) preceded by the introduction of the Statistical Learning framework which acts as a common reference framework for the entire course.
  
 
===Teachers===
 
===Teachers===
  
−
The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.
+
The course is composed of a blending of lectures and exercises by the course teacher and a teaching assistant.
  
−
* [http://www.deib.polimi.it/ita/personale/dettagli/267262 Matteo Matteucci]: the course teacher
+
* [http://www.deib.polimi.it/ita/personale/dettagli/267262 Matteo Matteucci]: the course teacher and here it is his [http://politecnicomilano.webex.com/join/matteo.matteucci webex room]
−
* [http://www.deib.polimi.it/ita/personale/dettagli/910274 Marco Cannici]: the teaching assistant
+
* [http://www.deib.polimi.it/ita/personale/dettagli/910274 Marco Cannici]: the teaching assistant and here it is his [HTTP://politecnicomilano.webex.com/join/marco.cannici webex room]
  
 
===Course Program===
 
===Course Program===
Line 31: Line 35:
 
The course mostly follows the following book which is also available for download in pdf
 
The course mostly follows the following book which is also available for download in pdf
  
−
* [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
+
* [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
  
 
The course lectures will present the theory and practice of the following:
 
The course lectures will present the theory and practice of the following:
−
* Machine Learning and Pattern Classification: the general concepts of Machine Learning and Pattern Recognition are introduced within the framework of Statistical Decision Theory with reference to the bias-variance trade off and the Bayes classifier;
+
* Machine Learning and Pattern Classification: the general concepts of Machine Learning and Pattern Recognition are introduced within the framework of Statistical Decision Theory with reference to the bias-variance trade-off and the Bayes classifier;
−
* Generalized Linear Regression: linear methods for regression will be presented and discussed introducing different techniques (e.g., Linear Regression, Ridge Regression, K-Nearest Neighbors Regression, Non Linear Regression, etc.) and the most common methodologies for model validation and selection (e.g., AIC, BIC, cross-validation, stepwise feature selection, Lasso, etc.).
+
* Generalized Linear Regression: linear methods for regression will be presented and discussed introducing different techniques (e.g., Linear Regression, Ridge Regression, K-Nearest Neighbors Regression, Non-Linear Regression, etc.) and the most common methodologies for model validation and selection (e.g., AIC, BIC, cross-validation, stepwise feature selection, Lasso, etc.).
−
* Linear and Non Linear Classification: generative and discriminative techniques for classification will be described and discussed (e.g., Logistic Regression, Linear and Quadratic Discriminant Analysis, K-Nearest Neighbors, Perceptron Rule and Support Vector Machines, etc.). Metrics for classifiers evaluation and comparison are presented in this part of the course (e.g., accuracy, precision, recall, ROC, AUC, F-measure, Matthew coefficient).
+
* Linear and Non-Linear Classification: generative and discriminative techniques for classification will be described and discussed (e.g., Logistic Regression, Linear and Quadratic Discriminant Analysis, K-Nearest Neighbors, Perceptron Rule, and Support Vector Machines, etc.). Metrics for classifiers evaluation and comparison are presented in this part of the course (e.g., accuracy, precision, recall, ROC, AUC, F-measure, Matthew coefficient).
 
* Unsupervised Learning: the most common approaches to unsupervised learning are described mostly focusing on clustering methods such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, etc
 
* Unsupervised Learning: the most common approaches to unsupervised learning are described mostly focusing on clustering methods such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, etc
  
Line 43: Line 47:
 
===Detailed course schedule===
 
===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!!
+
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
 
  Note: Lecture timetable interpretation
−
  * On Wednesday, in room ..., starts at 014:15 (cum tempore), ends at 17:15 or 18:15
+
  * On Wednesday, in room B.6.1, starts at 14:15 (cum tempore), ends at 18:15
−
  * On Thursday, in room ..., starts at 08:15 (cum tempore), ends at 10:15
+
  * On Thursday, in room B.6.1, starts at 08:15 (cum tempore), ends at 10:15
 +
 
 +
{| border="1" align="center" style="text-align:center;"
 +
|-
 +
|Date || Day || Time || Room || Teacher || Type || Topic
 +
|24/02/2021 || Wednesday || 15:15-19:15 || B.6.1 || Matteo Matteucci || Lecture || Course Intro + Machine Learning Intro
 +
|
 +
|25/02/2021 || Thursday || 08:15-10:15 || B.6.1 || Matteo Matteucci || Lecture || Statistical Learning Theory
 +
|
 +
|03/03/2021 || Wednesday || 15:15-19:15 || B.6.1 || Matteo Matteucci || Lecture || Statistical Learning Theory
 +
|
 +
|04/03/2021 || Thursday || 08:15-10:15 || B.6.1 || Matteo Matteucci || Lecture || Introduction to Linear Algebra
 +
|
 +
|03/03/2021 || Wednesday || 15:15-19:15 || B.6.1 || Matteo Matteucci || Lecture || Statistical Learning Theory
 +
|
 +
|03/03/2021 || Thursday || 08:15-10:15 || B.6.1 || Matteo Matteucci || Lecture || Introduction to Linear Algebra
 +
|
 +
 
 +
}
 +
 
 +
-- -- Wednesday, 03/03/2021
 +
3 Statistical machine learning 3 15:15-19:15 Wednesday, 03/03/2021
 +
2 Simple regression 2 08:15-10:15 Thursday, 04/03/2021
 +
-- -- Tuesday, 09/03/2021
 +
-- -- Wednesday, 10/03/2021
 +
3 Python + Numpy + Bias/Variance 3 15:15-19:15 Wednesday, 10/03/2021
 +
2 Simple regression 2 08:15-10:15 Thursday, 11/03/2021
 +
-- -- Tuesday, 16/03/2021
 +
-- -- Wednesday, 17/03/2021
 +
3 Multivariate regression 3 15:15-19:15 Wednesday, 17/03/2021
 +
2 Multivariate regression 2 08:15-10:15 Thursday, 18/03/2021
 +
-- -- Tuesday, 23/03/2021
 +
-- -- Wednesday, 24/03/2021
 +
3 Multivariae Linear Regression Laboratory 3 15:15-19:15 Wednesday, 24/03/2021
 +
2 Generalized linear regression 2 08:15-10:15 Thursday, 25/03/2021
 +
-- -- Tuesday, 30/03/2021
 +
-- -- Wednesday, 31/03/2021
 +
3 Feature selection 3 15:15-19:15 Wednesday, 31/03/2021
 +
2 Lasso 2 08:15-10:15 Thursday, 01/04/2021
 +
Vacanza -- Tuesday, 06/04/2021
 +
-- -- Wednesday, 07/04/2021
 +
3 Generalized linear regression and feature selection 3 15:15-19:15
 +
2 Classification KNN e Logistic regression 2 08:15-10:15 Thursday, 08/04/2021
 +
-- -- Tuesday, 13/04/2021
 +
-- -- Wednesday, 14/04/2021
 +
3 Logistic regression 3 15:15-19:15
 +
2 Linear discriminant analysis 2 08:15-10:15 Thursday, 15/04/2021
 +
-- -- Tuesday, 20/04/2021
 +
Prove Itinere -- Wednesday, 22/04/2020
 +
15:15-19:15
 +
Prove Itinere 08:15-10:15 Thursday, 23/04/2020
 +
-- -- Tuesday, 27/04/2021
 +
Appello Laurea -- Wednesday, 28/04/2021
 +
15:15-19:15
 +
2 Evaluation methods for classification 2 08:15-10:15 Thursday, 29/04/2021
 +
-- -- Tuesday, 04/05/2021
 +
-- -- Wednesday, 05/05/2021
 +
3 Logistic Regression, and LDA Laboratory 3 15:15-19:15
 +
2 Perceptron 2 08:15-10:15 Thursday, 06/05/2021
 +
-- -- Tuesday, 11/05/2021
 +
-- -- Wednesday, 12/05/2021
 +
3 SVM 3 15:15-19:15
 +
2 Introduction to Unsupervised 2 08:15-10:15 Thursday, 13/05/2021
 +
-- -- Tuesday, 18/05/2021
 +
-- -- Wednesday, 19/05/2021
 +
3 SVM and Classifiers Evaluation Laboratory 3 15:15-19:15
 +
2 Clustering 2 08:15-10:15 Thursday, 20/05/2021
 +
-- -- Tuesday, 25/05/2021
 +
-- -- Wednesday, 26/05/2021
 +
3 Clustering Laboratory 3 15:15-19:15
 +
2 Clustering 2 08:15-10:15 Thursday, 27/05/2021
 +
-- -- Tuesday, 01/06/2021
 +
-- Wednesday, 02/06/2021
 +
15:15-19:15
 +
2 Principal component analysis 2 08:15-10:15 Thursday, 03/06/2021
  
 +
<!--
 
{| border="1" align="center" style="text-align:center;"
 
{| border="1" align="center" style="text-align:center;"
 
|-
 
|-
Line 105: Line 184:
 
|04/06/2020 || Thursday || 08:15 - 10:15 || Teams Virtual Class ||  Theory || Matteo Matteucci || [https://web.microsoftstream.com/video/f58132ba-afd3-47ce-89c8-ba16782d8f37 Principal Component Analysis] (Ch. 10 ISL)
 
|04/06/2020 || Thursday || 08:15 - 10:15 || Teams Virtual Class ||  Theory || Matteo Matteucci || [https://web.microsoftstream.com/video/f58132ba-afd3-47ce-89c8-ba16782d8f37 Principal Component Analysis] (Ch. 10 ISL)
 
|}
 
|}
 +
-->
 
<!--
 
<!--
 
|-
 
|-

Revision as of 01:18, 24 February 2021


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

* 24/02/2021: Lectures start today


Course Aim & Organization

The objective of the Machine Learning course is to give an in-depth presentation of the techniques most used for pattern recognition, knowledge discovery, and data analysis/modeling. These techniques are presented both from a theoretical (i.e., statistics and information theory) perspective and a practical one (i.e., coding examples) through the descriptions of algorithms and their implementations in a general-purpose programming language (i.e., python).

The course presents the classical supervised and unsupervised learning paradigms described and discussed presenting regression, classification, and clustering problems in Bioinformatics. The course is composed of a set of lectures on specific machine learning techniques (e.g., generalized linear regression, logistic regression, linear and quadratic discriminant analysis, support vector machines, k-nearest-neighborhood, clustering, etc.) preceded by the introduction of the Statistical Learning framework which acts as a common reference framework for the entire course.

Teachers

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

Course Program

The course mostly follows the following book which is also available for download in pdf

The course lectures will present the theory and practice of the following:

  • Machine Learning and Pattern Classification: the general concepts of Machine Learning and Pattern Recognition are introduced within the framework of Statistical Decision Theory with reference to the bias-variance trade-off and the Bayes classifier;
  • Generalized Linear Regression: linear methods for regression will be presented and discussed introducing different techniques (e.g., Linear Regression, Ridge Regression, K-Nearest Neighbors Regression, Non-Linear Regression, etc.) and the most common methodologies for model validation and selection (e.g., AIC, BIC, cross-validation, stepwise feature selection, Lasso, etc.).
  • Linear and Non-Linear Classification: generative and discriminative techniques for classification will be described and discussed (e.g., Logistic Regression, Linear and Quadratic Discriminant Analysis, K-Nearest Neighbors, Perceptron Rule, and Support Vector Machines, etc.). Metrics for classifiers evaluation and comparison are presented in this part of the course (e.g., accuracy, precision, recall, ROC, AUC, F-measure, Matthew coefficient).
  • Unsupervised Learning: the most common approaches to unsupervised learning are described mostly focusing on clustering methods such as hierarchical clustering, k-means, k-medoids, Mixture of Gaussians, DBSCAN, etc

These topics will be presented both from a theoretical perspective and a practical one via implementations in the general-purpose programming language python.

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 Wednesday, in room B.6.1, starts at 14:15 (cum tempore), ends at 18:15
* On Thursday, in room B.6.1, starts at 08:15 (cum tempore), ends at 10:15
Date Day Time Room Teacher Type Topic 24/02/2021 Wednesday 15:15-19:15 B.6.1 Matteo Matteucci Lecture Course Intro + Machine Learning Intro 25/02/2021 Thursday 08:15-10:15 B.6.1 Matteo Matteucci Lecture Statistical Learning Theory 03/03/2021 Wednesday 15:15-19:15 B.6.1 Matteo Matteucci Lecture Statistical Learning Theory 04/03/2021 Thursday 08:15-10:15 B.6.1 Matteo Matteucci Lecture Introduction to Linear Algebra 03/03/2021 Wednesday 15:15-19:15 B.6.1 Matteo Matteucci Lecture Statistical Learning Theory 03/03/2021 Thursday 08:15-10:15 B.6.1 Matteo Matteucci Lecture Introduction to Linear Algebra

}

-- -- Wednesday, 03/03/2021 3 Statistical machine learning 3 15:15-19:15 Wednesday, 03/03/2021 2 Simple regression 2 08:15-10:15 Thursday, 04/03/2021 -- -- Tuesday, 09/03/2021 -- -- Wednesday, 10/03/2021 3 Python + Numpy + Bias/Variance 3 15:15-19:15 Wednesday, 10/03/2021 2 Simple regression 2 08:15-10:15 Thursday, 11/03/2021 -- -- Tuesday, 16/03/2021 -- -- Wednesday, 17/03/2021 3 Multivariate regression 3 15:15-19:15 Wednesday, 17/03/2021 2 Multivariate regression 2 08:15-10:15 Thursday, 18/03/2021 -- -- Tuesday, 23/03/2021 -- -- Wednesday, 24/03/2021 3 Multivariae Linear Regression Laboratory 3 15:15-19:15 Wednesday, 24/03/2021 2 Generalized linear regression 2 08:15-10:15 Thursday, 25/03/2021 -- -- Tuesday, 30/03/2021 -- -- Wednesday, 31/03/2021 3 Feature selection 3 15:15-19:15 Wednesday, 31/03/2021 2 Lasso 2 08:15-10:15 Thursday, 01/04/2021 Vacanza -- Tuesday, 06/04/2021 -- -- Wednesday, 07/04/2021 3 Generalized linear regression and feature selection 3 15:15-19:15 2 Classification KNN e Logistic regression 2 08:15-10:15 Thursday, 08/04/2021 -- -- Tuesday, 13/04/2021 -- -- Wednesday, 14/04/2021 3 Logistic regression 3 15:15-19:15 2 Linear discriminant analysis 2 08:15-10:15 Thursday, 15/04/2021 -- -- Tuesday, 20/04/2021 Prove Itinere -- Wednesday, 22/04/2020 15:15-19:15 Prove Itinere 08:15-10:15 Thursday, 23/04/2020 -- -- Tuesday, 27/04/2021 Appello Laurea -- Wednesday, 28/04/2021 15:15-19:15 2 Evaluation methods for classification 2 08:15-10:15 Thursday, 29/04/2021 -- -- Tuesday, 04/05/2021 -- -- Wednesday, 05/05/2021 3 Logistic Regression, and LDA Laboratory 3 15:15-19:15 2 Perceptron 2 08:15-10:15 Thursday, 06/05/2021 -- -- Tuesday, 11/05/2021 -- -- Wednesday, 12/05/2021 3 SVM 3 15:15-19:15 2 Introduction to Unsupervised 2 08:15-10:15 Thursday, 13/05/2021 -- -- Tuesday, 18/05/2021 -- -- Wednesday, 19/05/2021 3 SVM and Classifiers Evaluation Laboratory 3 15:15-19:15 2 Clustering 2 08:15-10:15 Thursday, 20/05/2021 -- -- Tuesday, 25/05/2021 -- -- Wednesday, 26/05/2021 3 Clustering Laboratory 3 15:15-19:15 2 Clustering 2 08:15-10:15 Thursday, 27/05/2021 -- -- Tuesday, 01/06/2021 -- Wednesday, 02/06/2021 15:15-19:15 2 Principal component analysis 2 08:15-10:15 Thursday, 03/06/2021

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 (up to 6 points)
  • WE: A written examination covering the whole program (up to 26 points)

the final score will be the sum of HW (not compulsory) and WE scores.

Due to the COVID-19 situation the written exam for the 2019/2020 edition has been turned into an oral examination:

  • You will get the oral grade as a mark in the scale of 30 up to 32/30 which you have to multiply by 0.8125 and then you add to it the score of the project. For your convenience here it is a conversion table with the final mark in case you do not turn in the project or you get the whole 6 marks in the project.

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:

  • [2020] 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.
  • [2020] Statistical Learning Introduction: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)
  • [2020] Linear Regression: Simple Linear Regression and Multiple Linear Regression. Generalized Linear models. Cross-validation techniques. Feature selection. Ridge Regression and Lasso.
  • [2020] Linear Classification: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods. Discriminative vs. generative methods. Support Vector Machines.
  • [2020] Clustering: Introduction to unsupervised learning and clustering, hierarchical clustering, k-means, DBSCNA, indexes for clustering evaluation.
  • [2020] Principal Component Analysis: Principal Component Analysis, Geometric Interpretation, Singular Values Decomposition.

Laboratories:

Additional Resources

Papers and links useful to integrate the textbook

  • Basic Linear Algebra: "Basic Linear Algebra" chapter from Wayne Winston book "Operations Research Applications and Algorithms (4th ed.)"
  • 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.

Online Resources

The following are links to online sources which might be useful to complement the material above

  • Statistical Learning MOOC covering the entire ISL book offered by Trevor Hastie and Rob Tibshirani. Start anytime in self-paced mode.
  • 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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