CS-433 / 7 credits

Teacher(s): Jaggi Martin, Flammarion Nicolas Henri Bernard

Language: English

## Summary

Machine learning methods 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, pattern recognition, deep learning, neural networks, data mining, knowledge discovery, algorithms

## Required courses

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

## Recommended courses

• Introduction to machine learning (CS-233)
• ...or similar bachelor lecture from other sections

## 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 models: Regression, classification, clustering, dimensionality reduction, neural networks, time-series analysis
• 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
• Conduct a real-world interdisciplinary machine learning project, in collaboration with application domain experts

## 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

No

## Notes/Handbook

https://github.com/epfml/ML_course

## In the programs

• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Exam form: Written (winter session)
• Subject examined: Machine learning
• Lecture: 4 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 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22