CS-233(b) / 4 credits

Teacher: Fua Pascal

Language: 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.

## Keywords

Machine learning, classification, regression, algorithms

Linear algebra

## Important concepts to start the course

• 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
• 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.

• Lectures
• Lab sessions

## Expected student activities

• Attend lectures
• Attend lab sessions and work on the weekly theory and coding exercises

## Assessment methods

• Written final exam

## Supervision

 Others Course website

## In the programs

• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Introduction to machine learning (BA4)
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Introduction to machine learning (BA4)
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Introduction to machine learning (BA4)
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Introduction to machine learning (BA4)
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Introduction to machine learning (BA4)
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Introduction to machine learning (BA4)
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks

## Reference week

 Mo Tu We Th Fr 8-9 CM3 9-10 10-11 CE1100CE1101CE1103 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22

Tuesday, 8h - 10h: Lecture CM3

Tuesday, 10h - 12h: Exercise, TP CE1100
CE1101
CE1103