2EL1730: Machine Learning

Course Overview

General Info: 2nd year course, CentraleSupélec, November 2019 - January 2020

Lecture hours: Tuesday (8:30-12:00), Friday (13:45 - 17:00)
Instructors: Fragkiskos Malliaros and Maria Vakalopoulou
Office hours: Right after class (or send us an email and we will find a good time to meet)

TAs: Mohamed El Amine Seddik, Yunshi Huang, Yoann Pradat, Jun Zhu

Piazza: piazza.com/centralesupelec/winter2020/2el1730/home

Machine learning is the scientific field that provides computers the ability to learn without being explicitly programmed (definition by Wikipedia). Machine learning lies at the heart of many real-world applications, including recommender systems, web search, computer vision, autonomous cars and automatic language translation.

The course will provide an overview of fundamental topics as well as important trends in machine learning, including algorithms for supervised and unsupervised learning, dimensionality reduction methods and their applications. A substantial lab section will involve group projects on a data science competition and will provide the students the ability to apply the course theory to real-world problems.

Schedule and Lectures

The topics of the lectures are subject to change (the following schedule outlines the topics that will be covered in the course). The slides for each lecture will be posted in piazza just before the start of the class. The due dates of the assignments/project are subject to change.

Lecture Date Topic Material Assignments/Project
1November 26Introduction; Model selection and evaluationLecture 1
2November 29Dimensionality reductionLecture 2
3December 3Linear and logistic regressionLecture 3Assignment 1 out
4December 6Probabilistic classifiers and linear discriminant analysisLecture 4
5December 13Non-parametric learning and nearest neighbor methodsLecture 5 Project proposal due on December 13
Assignment 2 out
6December 17Support Vector MachinesLecture 6Assignment 1 due on December 17
7December 20Tree-based methods and ensemble learningLecture 7
8January 7Neural networksLecture 8
9January 10Introduction to deep learning
Guest lecture by Dr. Stergios Christodoulidis (Institut Gustave Roussy)
Lecture 9
10January 14Introduction to reinforcement learning
Guest lecture by Dr. Nikolaos Tziortziotis (Tradelab R&D)
Lecture 10Assignment 2 due on January 14
11January 17Unsupervised learning: clusteringLecture 11
12January 20ExamsProject final report due on January 26

[November 26] Lecture 1: Introduction; Model selection and evaluation

Introduction to machine learning, administrivia, course structure and overview of the topics that will be covered in the course. Overfitting and generalization. Bias-variance tradeoff. Training, validation and test sets. Cross-validation. Evaluation of supervised learning algorithms. Basic concepts in optimization.


[November 29] Lecture 2: Dimensionality reduction

Dimemensionality reduction techniques. Singular Value Decomposition (SVD). Principal Component Analysis (PCA). Multidimensional Scaling (MDS) and nonlinear dimensionality reduction.


[December 3] Lecture 3: Linear and logistic regression

Supervised learning models. Linear regression. Regularization. Linear classification models. Logistic regression. Maximum likelihood estimation.

Reading: Additional:

[December 6] Lecture 4: Probabilistic classifiers and linear discriminant analysis

Bayes rule. Naive Bayes classifier. Maximum a posteriori estimation. Linear discriminant analysis (LDA).

Reading: Additional:

[December 13] Lecture 5: Non-parametric learning and nearest neighbor methods

Introduction to non-parametric learning methods. Distance and similarity metrics. Nearest neighbor algorithms.

Reading: Additional:

[December 17] Lecture 6: Support Vector Machines

Maximum margin classifier. Linear SVMs. Primal and dual optimization problems. Non-linearly separable data and the kernel trick. Regularization and the non-separable case.

Reading: Additional:

[December 20] Lecture 7: Tree-based methods and ensemble learning

Decision trees. Ensemble learning. Bagging and Boosting. The AdaBoost algorithm.

Reading: Additional:

[January 7] Lecture 8: Neural networks

Introduction to neural networks. The perceptron algorithm. Multilayer perceptron. Backpropagation. Applications.


[January 19] Lecture 9: Introduction to deep learning

Deep learning, CNNs


[January 14] Lecture 10: Introduction to reinforcement learning

Intelligence agents, dynamic programming, Monte Carlo methods, temporal difference learning


[January 17] Lecture 11: Unsupervised learning: clustering

Introduction to unsupervised learning methods. Data clustering. Hierarchical clustering. k-means clustering. Spectral clustering.


Course Structure and Objectives

Each section of the course is divided into 1h30' lecture and 1h30' lab. The labs will include hands-on assignments (using Python) and will provide the students the opportunity to deal with ML tasks in practice.

Learning objectives

The course aims to introduce students to the field of machine learning by:
  • Covering a wide range of topics, methodologies and related applications.
  • Giving the students the opportunity to obtain hands-on experience on dealing with.
We expect that by the end of the course, the students will be able to:
  • Identify problems that can be solved using machine learning methodologies.
  • Given a problem, identify and apply the most appropriate algorithm(s).
  • Implement some of those algorithms from scratch.
  • Evaluate and compare machine learning algorithms for a particular task.
  • Deal with real-world data challenges.


There is no official prerequisite for this course. However, the students are expected to:

  • Have basic knowledge of probability theory and linear algebra.
  • Be familiar with at least one programming language (e.g., Python or any language of their preference).

Reading material

There is no single requiered textbook for the course. We will recommend specific chapters from the following books:


The evaluation of the course will be based on the following:

  1. Two assignments: the assignments will include theoretical questions as well hands-on practical questions that will familiarize the students with basic machine learning tasks.
  2. Project: The students are expected to form groups of 3-4 people, propose a topic for their project, and submit a final project report. Please, read the project section for more details.
  3. Final exam: Final exam in the material covered in the course.

The grading will be as follows:

Assignment 1 (individually): 10%
Assignment 2 (groups of 3-4 students): 20%
Project (groups of 3-4 students): 30%
Final exam: 40%

Academic integrity

All of your work must be your own. Don't copy another student's assignment, in part or in total, and submit it as your own work. Acknowledge and cite source material in your papers or assignments.


Details about the project of the course have been posted on piazza.



Software tools

Related conferences
Please find below a list of conferences related to the contents of the course (mostly in the field of machine learning and data mining. We provide the DBLP website of each venue where you can access the proceedings (papers, tutorials, etc).

Check out the website of each conference (e.g., KDD 2016 ) for more information.