Introduction to machine learning

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

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

Resources

Moodle Link

• https://go.epfl.ch/CS-233