# Coursebooks

## Machine learning

#### Lecturer(s) :

Flammarion Nicolas Henri Bernard
Jaggi Martin

English

#### Summary

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

#### Content

1. Basic regression and classification concepts and methods: Linear models, overfitting, linear regression, Ridge regression, logistic regression, and k-NN.
2. Fundamental concepts: cost-functions and optimization, cross-validation and bias-variance trade-off, curse of dimensionality.
3. Unsupervised learning: k-Means Clustering, Gaussian mixture models and the EM algorithm.
4. Dimensionality reduction: PCA and matrix factorization, word embeddings
5. Advanced methods: generalized linear models, SVMs and Kernel methods, Neural networks and deep learning

#### Keywords

• Machine learning, pattern recognition, deep learning, data mining, knowledge discovery, algorithms

#### Learning Prerequisites

##### Required courses

• Analysis I, II, III
• Linear Algebra
• Probability and Statistics (MATH-232)
• Algorithms (CS-250)

##### Recommended courses

• Introduction to differentiable optimization (MATH-265)
• Linear Models (MATH-341)

##### Important concepts to start the course

• Basic probability and statistics (conditional and joint distribution, independence, Bayes rule, random variables, expectation, mean, median, mode, central limit theorem)
• Basic linear algebra (matrix/vector multiplications, systems of linear equations, SVD)
• Multivariate calculus (derivative w.r.t. vector and matrix variables)
• Basic Programming Skills (labs will use Python)

#### Learning Outcomes

By the end of the course, the student must be able to:
• Define the following basic machine learning problems: Regression, classification, clustering, dimensionality reduction, time-series
• Explain the main differences between them
• Implement algorithms for these machine learning models
• Optimize the main trade-offs such as overfitting, and computational cost vs accuracy
• Implement machine learning methods to real-world problems, and rigorously evaluate their performance using cross-validation. Experience common pitfalls and how to overcome them
• Explain and understand the fundamental theory presented for ML methods

#### Teaching methods

• Lectures
• Lab sessions
• Course Projects

#### Expected student activities

Students are expected to:

• attend lectures
• attend lab sessions and work on the weekly theory and coding exercises
• work on projects using the code developed during labs, in small groups

#### Assessment methods

• Written final exam
• Continuous control (Course projects)

#### Supervision

 Office hours Yes Assistants Yes Forum Yes

#### Resources

No

##### Notes/Handbook

https://github.com/epfml/ML_course

### Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15   INF119
INF2
INJ218
INM202
INR219

15-16
16-17   SG1
17-18 SG1
18-19
19-20
20-21
21-22

Lecture
Exercise, TP
Project, other

### legend

• Autumn semester
• Winter sessions
• Spring semester
• Summer sessions
• Lecture in French
• Lecture in English
• Lecture in German