Course Schedule

wk	Lecture	Notes	Links	Reading material
9/1	Building Machine that 'learn'
9/6	Essential Math and Formalism for ML	HW1 Release (Math Foundations for ML)
9/8	Philosophical and Cognitive underpinnings of ML
9/13	Linear Regression and Regularization (Part 1)
9/15	Linear Regression and Regularization (Part 2)
9/20	Logistic Regression (Part 1)	HW 1 Due HW2 Release (Linear Regularization)
9/22	Logistic Regression (Part 2)
9/27	Support Vector Machines (Part 1)
9/29	Support Vector Machines (Part 2)
10/4	Decision Trees for Classification	HW 2 Due HW3 Release (SVMs)
10/6	Random Forests for Classification
10/13	Non-Linear Regression and Gradient Descent
10/18	Gradient Descent and its Variants	HW 3 Due HW4 Release (Random Forests)
10/20	Perceptrons (Part 1)
10/25	Perceptrons (Part 2)	Capstone Project Released
10/27	Convolutional Neural Networks (Part 1)
11/1	Convolutional Neural Networks (Part 2)
11/3	Recurrent Neural Networks (Part 1)	HW 4 Due HW5 Release (CNNs)
11/8	Recurrent Neural Networks (Part 2)
11/10	Dimensionality Reduction (Part 1)
11/15	Dimensionality Reduction (Part 2)
11/17	Clustering (Part 1)	HW 5 Due HW6 Release (Dimension reduction)
11/22	Clustering (Part 2)
11/29	Reinforcement Learning Foundations (Part 1)
12/1	Reinforcement Learning Foundations (Part 2)
12/6	Policy Gradients
12/8	Frontiers of ML (research discussions by members of CILVR)
12/13	Frontiers of ML
12/14		Capstone Project Due