Course Material & Lectures

📖 Course Material

It is in the form of Jupyter Notebook slides with math, code, drawings, plots and explanations

• Slides and material will be uploaded before every lecture on Google Classroom and here.
• Good starting point but but may be not enough.
• Textbooks are required.

Lectures

Opening the Binder link will reproduce the slides live.

• Go forward pressing Space
• Backward with Shift+Space.

Be patient, it takes a while to load Binder. Thanks.

Contributors

Material

Date	Topic	Slides	Github/HTML
	Introduction
Feb 21, 23	Introduction to ML
Feb 28, Mar 2	The geometry of linear maps
	Unsupervised Learning
March 7, 9	Eigendecomposition, PCA, Intro to 3DMM
March 9, 14	PCA high dim
March 21, 23	Kmeans
March 30, April 4	MLE, GMM
April 13, 18	GMM, density estimator
	Supervised Learning, Non-Parametric
April 18, 20	KNN
April 20, 27	Decision Trees and Random Forest
May 2, 4	Model Selection and Eval Metrics
	Supervised Learning, Parametric
May 9, 11	Linear, Polynomial, Ridge Regression
May 16, 18	Perceptron, Logistic Regression
May 23, 25	Softmax, MLP
May 25	Backpropagation