CSE204 - Machine Learning
Computer Science Bachelor Program
École Polytechnique
Information:
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
Team:
Jesse Read (lecturer), Adrien Ehrhardt (chef de TD), Davide Buscaldi (chef de TD), Ekaterina Antonenko (assistant), Aleksa Marusic (assistant)
Grading:
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)
Moodle:
https://moodle.polytechnique.fr/course/view.php?id=12838
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)