2EL1730: Machine Learning

Course Overview

General Info: 2nd year course, CentraleSupélec, November 2021 - January 2022, 35 hours

Lecture hours: Tuesday (8:30-11:45), Friday (13:45 - 17:00)
Instructors: Fragkiskos Malliaros and Maria Vakalopoulou
Online office hours: Right after class (or send me an email and we will find a good time to meet)

TAs: Hakim Benkirane, Stuti Jain, Lilly Monier, Jun Zhu

Edunao: https://centralesupelec.edunao.com/course/view.php?id=4281

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 on Edunao 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 30Introduction; Model selection and evaluationLecture 1
2December 3Linear and logistic regressionLecture 2
3December 7Probabilistic classifiers and linear discriminant analysisLecture 3Assignment 1 out
4December 10Non-parametric learning and nearest neighbor methodsLecture 4
5December 17Tree-based methods and ensemble learningLecture 5Assignment 1 due on December 21
6January 4Support Vector MachinesLecture 6Kaggle project out
7January 7Neural networksLecture 7Assignment 2 out
8January 11Introduction to deep learning
Lecture 8
9January 14Dimensionality reductionLecture 9
10January 18Unsupervised learning: clusteringLecture 10Assignment 2 due on January 21
11January 21Topics in unsupervised learning
Guest lecture
Lecture 11
12January 24ExamsKaggle project due on January 30

[November 30] 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.


[December 3] Lecture 2: Linear and logistic regression

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

Reading: Additional:

[December 7] Lecture 3: Probabilistic classifiers and linear discriminant analysis

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

Reading: Additional:

[December 10] Lecture 4: 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 5: Tree-based methods and ensemble learning

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

Reading: Additional:

[January 4] 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.


[January 7] Lecture 7: Neural networks

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


[January 11] Lecture 8: Introduction to deep learning

Deep learning, CNNs


[January 14] Lecture 9: Dimensionality reduction

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


[January 18] Lecture 10: Unsupervised learning: clustering

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


[January 21] Lecture 11: Topics in unsupervised learning

Nonnegative matrix factorization (NMF). Loss functions for NMF. Multiplicative algorithms.

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: this will be a Kaggle challenge. The students are expected to form groups of 3-4 people, work on the challenge, and submit a final report.
  3. Final exam: Final exam in the material covered in the course.

The grading will be as follows:

Assignment 1 (individually): 10%
Assignment 2 (individually): 10%
Kaggle project (groups of 3-4 students): 20%
Final exam: 60%

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 Edunao.



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 2020 ) for more information.