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 ;-)
 +
* 20/07/2022: Grades for the three homeworks are [[Media:ML2122-Grades_HWs.pdf|HERE!]]
 +
* 28/06/2022: Grades for the second homework are [[Media:ML2122-Grades_HW1+2.pdf|HERE!]]
 +
* 14/06/2022: The third homework is out!!!
 +
* 03/06/2022: Updates slides, and added a note about the oral exam including a detailed syllabus for it
 +
* 02/06/2022: Last lecture of the course will be online on Friday 03/06/2022 from 14:00 to 16:00
 +
* 31/05/2022: Poll for the last lecture is [https://forms.gle/u1STPsJgekMKx99K6 here!]
 +
* 29/05/2022: Grades for the first homework are [[Media:ML2122-Grades_HW1_v2.pdf|HERE!]]
 +
* 21/05/2022: The dataset for the second homework is changed!!!
 +
* 19/05/2022: The second homework is out!!!
 +
* 04/05/2022: Changed lecture schedule
 +
* 02/05/2022: Changed deadline first homework -> 11/05/2022 at noon
 
  * 22/04/2022: The first homework is out!!!
 
  * 22/04/2022: The first homework is out!!!
 
  * 08/03/2022: Added material for the first lab
 
  * 08/03/2022: Added material for the first lab
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|06/05/2022 || Thursday || 12:15-14:15 || 26.01 || Matteo Matteucci || Lecture || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)  
 
|06/05/2022 || Thursday || 12:15-14:15 || 26.01 || Matteo Matteucci || Lecture || Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)  
 
|-
 
|-
|11/05/2022 || Wednesday || 15:15-18:15 || 26.02 ||Stefano Samele || Python Laboratory || SVM and Classifiers Evaluation Laboratory
+
|11/05/2022 || Wednesday || 15:15-18:15 || 26.02 || Matteo Matteucci || Lecture || Unsupervised Learning and Clustering (Ch. 10 ISL)
 
|-
 
|-
|12/05/2022 || Thursday || 12:15-14:15 || 26.01 || Matteo Matteucci || Lecture || Unsupervised Learning and Clustering (Ch. 10 ISL)
+
|12/05/2022 || Thursday || 12:15-14:15 || 26.01 || Matteo Matteucci || Lecture || Clustering (Ch. 10 ISL)
 
|-
 
|-
|18/05/2022 || Wednesday || 15:15-18:15 || 26.02 || Matteo Matteucci || Lecture || Clustering (Ch. 10 ISL)
+
|18/05/2022 || Wednesday || 15:15-18:15 || 26.02 || Stefano Samele || Python Laboratory  || SVM and Classifiers Evaluation Laboratory
 
|-
 
|-
|19/05/2022 || Thursday || 12:15-14:15 || 26.01 || Matteo Matteucci || Lecture || Clustering Evaluation
+
|19/05/2022 || Thursday || --- || --- || --- || --- || --- No Lecture ---
 
|-
 
|-
 
|25/05/2022 || Wednesday || 15:15-18:15 || 26.02 || Stefano Samele || Python Laboratory || Clustering Laboratory
 
|25/05/2022 || Wednesday || 15:15-18:15 || 26.02 || Stefano Samele || Python Laboratory || Clustering Laboratory
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|26/05/2022 || Thursday || --- || --- || --- || --- || --- No Lecture ---
 
|26/05/2022 || Thursday || --- || --- || --- || --- || --- No Lecture ---
 
|-
 
|-
|01/06/2022 || Wednesday || 15:15-18:15 || 26.02 || Matteo Matteucci || Lecture || Principal Component Analysis (Ch. 10 ISL)
+
|01/06/2022 || Wednesday || 15:15-18:15 || 26.02 || Matteo Matteucci || Lecture || Clustering Evaluation
 
|-
 
|-
 
|02/06/2022 || Thursday || --- || --- || --- || --- || --- No Lecture ---
 
|02/06/2022 || Thursday || --- || --- || --- || --- || --- No Lecture ---
 
|-
 
|-
|08/06/2022 || Wednesday || 15:15-18:15 || 26.02 || Matteo Matteucci || Lecture || <Spare>
+
|03/06/2022 || Friday || 14:00-16:00 || Online || Matteo Matteucci || Lecture || Principal Component Analysis (Ch. 10 ISL)
 
|}
 
|}
  
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the final score will be the sum of HW (not compulsory) and OR scores. 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 [[Media:ML2021-Conversion.pdf|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.
 
the final score will be the sum of HW (not compulsory) and OR scores. 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 [[Media:ML2021-Conversion.pdf|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.
 +
 +
====Notes about oral exams====
 +
 +
Oral exams will happen either on the day of the exam indicated by the information system of the university or one of the followings. Once registrations are closed, I will send you an online document with the slots available for the oral exam and you will have to select your preferred slot. I usually interview two people at a time and the slot is 1 hour.
 +
 +
At [https://docs.google.com/document/d/14_K0CcyjRcZXJWBE5WwhsadL3cTUjc5__1x2nDL_G7E/edit?usp=sharing this link] you can find a detailed syllabus of the course you can use to double-check your preparation before the oral exam ;-)
  
 
====Homework #1 2021/2022====
 
====Homework #1 2021/2022====
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The project should consist of an executable python notebook, sufficiently commented. Most of the cells should be adequately followed or preceded by an explanation of what you are doing, why, what the output is, how you interpret it, and so on.
 
The project should consist of an executable python notebook, sufficiently commented. Most of the cells should be adequately followed or preceded by an explanation of what you are doing, why, what the output is, how you interpret it, and so on.
  
You can submit your notebook via email at stefano.samele@polimi.it. The deadline is 06/05/22 at midday CET.
+
You can submit your notebook via email at stefano.samele@polimi.it. The deadline is 11/05/22 at midday CET.
 
The notebook should already display all the cells' outputs. A copy and a re-execution will be performed to evaluate your work: we need to be sure your code is actually executable and displays consistent results.
 
The notebook should already display all the cells' outputs. A copy and a re-execution will be performed to evaluate your work: we need to be sure your code is actually executable and displays consistent results.
  
 
You can attend the project in a group of maximum two. Scores (0-1-2, with half points) will be assigned based on the ability to organize the investigation and the code, adopt relevant methodologies presented during laboratories, understand and interpret results, and comply with project requests.
 
You can attend the project in a group of maximum two. Scores (0-1-2, with half points) will be assigned based on the ability to organize the investigation and the code, adopt relevant methodologies presented during laboratories, understand and interpret results, and comply with project requests.
 +
 +
====Homework #2 2021/2022====
 +
 +
The second project asks you to develop a classification model for the <s>Autism Screening Adult Data Set</s> [[Media:ML-2122-HW2.2.zip | Breast Cancer Wisconsin Dataset]]. The task consists in predicting whether the patient is affected by an Autistic Spectrum Disorder, based on a set of specific features. The goal of the project is to build, given the set of models explained during the laboratories, the best fit for the task. 
 +
 +
You can submit your notebook via email at stefano.samele@polimi.it. The deadline is <s>01/06/22</s> 03/06/22 at midday CET.
 +
Please rename your file as name_surname_stuedentidnumber.
 +
 +
For any other information please refer to the previous project announcement.
 +
 +
====Homework #3 2021/2022====
 +
 +
The third project asks you to investigate the [https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression Mice Protein Expression Dataset]. This dataset consists of the expression levels of 77 proteins/protein modifications that produced detectable signals in the nuclear fraction of cortex in response to learning processes. These proteins expression levels are registered for eight classes of different mice (for a better explanation, you can check the official UCI page or the [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0129126 following paper]).
 +
 +
The objectives are:
 +
1) Study with clustering techniques seen during laboratories if there are structures in the data that allow samples from different classes to be easily identified;
 +
2) Compare the performance of different clustering algorithms using the metrics presented during the course.
 +
 +
You can submit your notebook via email at stefano.samele@polimi.it. The deadline is 28/06/22 at midday CET. Please rename your file as name_surname_stuedentidnumber.
 +
Please remember that the only submission format accepted is the jupyter notebook.
 +
For any other information, please refer to the previous projects announcements.
  
 
==Teaching Material (the textbook)==  
 
==Teaching Material (the textbook)==  
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* [[Media:ML-2122-01-StatisticalLearning.pdf | [2021/2022] 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-2122-01-StatisticalLearning.pdf | [2021/2022] 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-2122-02-LinearRegression.pdf | [2021/2022] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Generalized Linear models. Cross-validation techniques. Feature selection. Ridge Regression and Lasso.
 
* [[Media:ML-2122-02-LinearRegression.pdf | [2021/2022] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Generalized Linear models. Cross-validation techniques. Feature selection. Ridge Regression and Lasso.
* [[Media:ML-2021-03-LinearClassification.pdf | [2020/2021] 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.  
+
* [[Media:ML-2122-03-LinearClassification.pdf | [2021/2022] 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.  
* [[Media:ML-2021-04-Clustering.pdf | [2020/2021] Clustering]]: Introduction to unsupervised learning and clustering, hierarchical clustering, k-means, DBSCNA, indexes for clustering evaluation.  
+
* [[Media:ML-2122-04-Clustering.pdf | [2021/2022] Clustering]]: Introduction to unsupervised learning and clustering, hierarchical clustering, k-means, DBSCNA, indexes for clustering evaluation.  
* [[Media:ML-2021-05-PrincipalComponentAnalysis.pdf | [2020/2021] Principal Component Analysis]]: Principal Component Analysis, Geometric Interpretation, Singular Values Decomposition.
+
* [[Media:ML-2122-05-PrincipalComponentAnalysis.pdf | [2021/2022] Principal Component Analysis]]: Principal Component Analysis, Geometric Interpretation, Singular Values Decomposition ([https://politecnicomilano.webex.com/webappng/sites/politecnicomilano/recording/720e97cac563103abcff005056821bc9/playback Here you find the video of the lecture]]).
  
 
===Laboratories===
 
===Laboratories===
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*[[Media:ML2122-lab03.zip | [2021/2022] Material for the third lab session]]: some useful jupiter notebooks which will be used during the third lab session ([[Media:ML2122-lab03_solutions_part1.zip |complete version]])
 
*[[Media:ML2122-lab03.zip | [2021/2022] Material for the third lab session]]: some useful jupiter notebooks which will be used during the third lab session ([[Media:ML2122-lab03_solutions_part1.zip |complete version]])
 
*[[Media:ML2122-lab04.zip | [2021/2022] Material for the fourth lab session]]: some useful jupiter notebooks which will be used during the fourth lab session ([[Media:ML2122-lab04_solutions.zip |complete version]])
 
*[[Media:ML2122-lab04.zip | [2021/2022] Material for the fourth lab session]]: some useful jupiter notebooks which will be used during the fourth lab session ([[Media:ML2122-lab04_solutions.zip |complete version]])
 +
*[[Media:ML2122-lab05.zip | [2021/2022] Material for the fifth lab session]]: some useful jupiter notebooks which will be used during the fifth lab session ([[Media:ML2122-lab05_solutions.zip |complete version]])
 +
*[[Media:ML2122-lab06.zip | [2021/2022] Material for the sixth lab session]]: some useful jupiter notebooks which will be used during the sixth lab session ([[Media:ML2122-lab06_solutions.zip |complete version]])
 
<!--
 
<!--
 
*[[Media:ML2021-lab01.zip | [2020/2021] Material for the first lab session]]: some useful jupiter notebooks which will be used during the first lab session ([[Media:ML2021-lab01_solutions.zip |complete version]])
 
*[[Media:ML2021-lab01.zip | [2020/2021] Material for the first lab session]]: some useful jupiter notebooks which will be used during the first lab session ([[Media:ML2021-lab01_solutions.zip |complete version]])

Latest revision as of 21:00, 20 July 2022


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

* 20/07/2022: Grades for the three homeworks are HERE!
* 28/06/2022: Grades for the second homework are HERE!
* 14/06/2022: The third homework is out!!!
* 03/06/2022: Updates slides, and added a note about the oral exam including a detailed syllabus for it
* 02/06/2022: Last lecture of the course will be online on Friday 03/06/2022 from 14:00 to 16:00
* 31/05/2022: Poll for the last lecture is here!
* 29/05/2022: Grades for the first homework are HERE!
* 21/05/2022: The dataset for the second homework is changed!!!
* 19/05/2022: The second homework is out!!!
* 04/05/2022: Changed lecture schedule
* 02/05/2022: Changed deadline first homework -> 11/05/2022 at noon
* 22/04/2022: The first homework is out!!!
* 08/03/2022: Added material for the first lab
* 06/03/2022: Slides from first lectures and last year's recordings updated  
* 23/02/2022: 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 26.02, starts at 15:15 (cum tempore), ends at 18:15
* On Thursday, in room 26.01, starts at 12:15 (cum tempore), ends at 14:15


Date Day Time Room Teacher Type Topic
23/02/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture Machine Learning Intro
24/02/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Statistical Learning Theory (Ch. 1 ISL)
02/03/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture Statistical Learning Theory - Bias/Variance Trade-off (Ch. 2 ISL)
03/03/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Introduction to Linear Algebra
09/03/2022 Wednesday 15:15-18:15 26.02 Stefano Samele Python Laboratory Python + Numpy + Bias/Variance
10/03/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Simple Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
16/03/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture Multi Variate Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
17/03/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Multi Variate Linear Regression] (Ch. 2 ISL + Ch. 3 ISL)
23/03/2022 Wednesday 15:15-18:15 26.02 Stefano Samele Python Laboratory Multivariate Linear Regression Laboratory
24/03/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Generalized Linear Regression (Ch. 2 ISL + Ch. 3 ISL)
30/03/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture Feature Selection and Model Assessment (Ch. 3 + Ch. 6 ISL)
31/03/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Ridge Regression and Lasso (Ch. 3 + Ch. 6 ISL)
06/04/2022 Wednesday 15:15-18:15 26.02 Stefano Samele Python Laboratory Generalized Linear Regression and Feature Selection Laboratory
07/04/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Classification: KNN and Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
13/04/2022 Wednesday --- --- --- --- --- No Lecture ---
14/04/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Classification: Logistic Regression (Ch. 4 ISL + Ch. 4 ESL)
20/04/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture Classification: Linear Discriminanr Analysis (Ch. 4 ISL)
21/04/2022 Thursday 12:15 - 14:15 26.01 Matteo Matteucci Lecture Evaluation methods for classification
27/04/2022 Wednesday 15:15-18:15 26.02 Stefano Samele Python Laboratory Logistic Regression and LDA Laboratory
28/04/2022 Thursday --- --- --- --- --- No Lecture ---
04/05/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture The Perceptron (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
06/05/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Support Vector Machines (Ch. 4 ESL, Ch. 9 ISL, Ch. 12 ESL)
11/05/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture Unsupervised Learning and Clustering (Ch. 10 ISL)
12/05/2022 Thursday 12:15-14:15 26.01 Matteo Matteucci Lecture Clustering (Ch. 10 ISL)
18/05/2022 Wednesday 15:15-18:15 26.02 Stefano Samele Python Laboratory SVM and Classifiers Evaluation Laboratory
19/05/2022 Thursday --- --- --- --- --- No Lecture ---
25/05/2022 Wednesday 15:15-18:15 26.02 Stefano Samele Python Laboratory Clustering Laboratory
26/05/2022 Thursday --- --- --- --- --- No Lecture ---
01/06/2022 Wednesday 15:15-18:15 26.02 Matteo Matteucci Lecture Clustering Evaluation
02/06/2022 Thursday --- --- --- --- --- No Lecture ---
03/06/2022 Friday 14:00-16:00 Online Matteo Matteucci Lecture Principal Component Analysis (Ch. 10 ISL)

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)
  • OR: An oral discussion covering the whole program (up to 26 points)

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

Notes about oral exams

Oral exams will happen either on the day of the exam indicated by the information system of the university or one of the followings. Once registrations are closed, I will send you an online document with the slots available for the oral exam and you will have to select your preferred slot. I usually interview two people at a time and the slot is 1 hour.

At this link you can find a detailed syllabus of the course you can use to double-check your preparation before the oral exam ;-)

Homework #1 2021/2022

The first project asks you to develop a regression model on the Forest Fires Data Set. The goal of the model is to predict, given a set of features the burned area of forest fires. The project should focus on investigating the part of features selection for the regression task to understand the relationships between the different variables involved fully. An accurate and helpful description of the dataset is included in the forestfire.names file.

The project should consist of an executable python notebook, sufficiently commented. Most of the cells should be adequately followed or preceded by an explanation of what you are doing, why, what the output is, how you interpret it, and so on.

You can submit your notebook via email at stefano.samele@polimi.it. The deadline is 11/05/22 at midday CET. The notebook should already display all the cells' outputs. A copy and a re-execution will be performed to evaluate your work: we need to be sure your code is actually executable and displays consistent results.

You can attend the project in a group of maximum two. Scores (0-1-2, with half points) will be assigned based on the ability to organize the investigation and the code, adopt relevant methodologies presented during laboratories, understand and interpret results, and comply with project requests.

Homework #2 2021/2022

The second project asks you to develop a classification model for the Autism Screening Adult Data Set Breast Cancer Wisconsin Dataset. The task consists in predicting whether the patient is affected by an Autistic Spectrum Disorder, based on a set of specific features. The goal of the project is to build, given the set of models explained during the laboratories, the best fit for the task.

You can submit your notebook via email at stefano.samele@polimi.it. The deadline is 01/06/22 03/06/22 at midday CET. Please rename your file as name_surname_stuedentidnumber.

For any other information please refer to the previous project announcement.

Homework #3 2021/2022

The third project asks you to investigate the Mice Protein Expression Dataset. This dataset consists of the expression levels of 77 proteins/protein modifications that produced detectable signals in the nuclear fraction of cortex in response to learning processes. These proteins expression levels are registered for eight classes of different mice (for a better explanation, you can check the official UCI page or the following paper).

The objectives are: 1) Study with clustering techniques seen during laboratories if there are structures in the data that allow samples from different classes to be easily identified; 2) Compare the performance of different clustering algorithms using the metrics presented during the course.

You can submit your notebook via email at stefano.samele@polimi.it. The deadline is 28/06/22 at midday CET. Please rename your file as name_surname_stuedentidnumber. Please remember that the only submission format accepted is the jupyter notebook. For any other information, please refer to the previous projects announcements.

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.

Laboratories

We will use Python (with Jupyter notebooks) throughout the course, thus we kindly ask you to install the "Anaconda" package to be ready for the labs. Here are the download links. You can find a simple Jupyter notebook HERE to test if the installation succeeded.

To open the notebook

  • launch the "Anaconda Navigator" app
  • launch the "jupyter Notebook" app within the navigator, it should automatically open a webpage (it may take a while)
  • on the webpage, navigate on the folder where you downloaded the "lab01.00-TestEnvironment.ipynb" file, and press on the file to open it
  • then follow the instruction within the notebook

If you didn't install anaconda but just jupyter, or if you can't find Anaconda Navigator

  • open a shell ("Anaconda Prompt" if you are using Windows)
  • move on to the folder where you downloaded "lab01.00-TestEnvironment.ipynb"
  • run the "jupyter notebook" command, it should automatically open a webpage
  • then on the webpage press on the "lab01.00-TestEnvironment.ipynb" file and follow the instructions within the notebook

The following are the notebook used in the labs:

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.

Python examples to better practice with numpy library

Year 2020/2021 Recordings (use at your own risk)

As I registered these due to pandemics, I am making them available. They DO NOT REPLACE THIS YEAR classroom lectures which are to be considered as the official material of this year, but they might be useful to double-check your notes.

Also, labs are available, however, the teaching assistant this year has changed.

Online Resources

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

  • An Introduction to Linear Algebra with numpy examples. It provides the very fundamental definitions, does not cover eigenvalues and eigenvectors.
  • 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)