CSE204 - Machine Learning

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

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)

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)