CSE204 - Machine Learning

Computer Science Bachelor Program

École Polytechnique


Course Outline: A working outline of topics (non-exhaustive):

  • Introduction to Machine Learning

  • Regression (least squares, polynomial regression, gradient descent, k-nearest neighbors)

  • Classification (logistic regression, k-nearest neigbors)

  • Overfitting and Regularization (ridge regression)

  • Neural Networks I

  • Neural Networks II (Multi-layer perceptrons and back propagation)

  • Deep Learning (and Convolutional neural networks)

  • Decision Trees and Ensemble Methods (C4.5, bagging, boosting, random forest)

  • Unsupervised Learning I (principal components analysis, auto encoders)

  • Unsupervised Learning II (k-means clustering, mixture models)

  • Kernel Methods (support vector machines, spectral clustering)

  • Reinforcement Learning


Jesse Read (lecturer), Adrien Ehrhardt (chef de TD), Davide Buscaldi (chef de TD), Ekaterina Antonenko (assistant), Aleksa Marusic (assistant)


Lab reports + quizzes: 50% (two in-class lab exams, one graded assignment, and minor points for lab completions)

Group project (in groups of 3): 50% (will be evaluated via approx. 30 minutes oral presentation at the end of the course)



GitHub repository for labs: https://github.com/adimajo/CSE204-2021

Recommended Readings:

(my picks)

Good book to review fundamentals: Mathematics for Machine Learning (Deisenroth et al.)

Focus on Linear Algebra: Linear Algebra and Learning from Data (Gilbert Strang)

The "bible" of Deep Learning: Deep Learning (Goodfellow et al.)

(page in construction)