Introduction to machine learning CS-233 / 6 crédits
Enseignant(s): Fua Pascal, Salzmann Mathieu
Langue: Anglais
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
Introduction: General concepts, data representation, basic optimization. Linear methods: Linear regression, least-square classification, logistic regression, linear SVMs. Nonlinear methods: Polynomial regression, kernel methods, K nearest neighbors Deep learning: Multi-layer perceptron, CNNs. Unsupervised learning: Dimensionality reduction, clustering. Learning Prerequisites
REQUIRED COURSES
Linear Algebra
IMPORTANT CONCEPTS TO START THE COURSE
Basic linear algebra (matrix/vector multiplications, systems of linear equations, SVD) Multivariate calculus (derivatives 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 Explain the main differences between them Derive the formulation of these machine learning models Assess / Evaluate the main trade-offs such as overfitting, and computational cost vs accuracy Implement machine learning methods on real-wolrd problems, and rigorously evaluate their performance using cross-validation Teaching methods
Lectures Pen-and-paper exercise sessions Python lab with a mini project in groups of 3 students Expected student activities
Attend lectures Attend lab sessions Work on the weekly theory and coding exercises Assessment methods
Self-assessment via the solutions of the pen-and-paper exercises and coding labs Two milestones for the mini-proejct (10% of the grade each) Final exam (80% of the grade) Supervision
Office hours No Assistants Yes Forum Yes Resources
MOODLE LINK