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Course Schedule

wkLectureNotesLinksReading material
9/1Building Machine that 'learn'
9/6Essential Math and Formalism for MLHW1 Release (Math Foundations for ML)
9/8Philosophical and Cognitive underpinnings of ML
9/13Linear Regression and Regularization (Part 1)
9/15Linear Regression and Regularization (Part 2)
9/20Logistic Regression (Part 1)HW 1 Due
HW2 Release (Linear Regularization)
9/22Logistic Regression (Part 2)
9/27Support Vector Machines (Part 1)
9/29Support Vector Machines (Part 2)
10/4Decision Trees for ClassificationHW 2 Due
HW3 Release (SVMs)
10/6Random Forests for Classification
10/13Non-Linear Regression and Gradient Descent
10/18Gradient Descent and its VariantsHW 3 Due
HW4 Release (Random Forests)
10/20Perceptrons (Part 1)
10/25Perceptrons (Part 2)Capstone Project Released
10/27Convolutional Neural Networks (Part 1)
11/1Convolutional Neural Networks (Part 2)
11/3Recurrent Neural Networks (Part 1)HW 4 Due
HW5 Release (CNNs)
11/8Recurrent Neural Networks (Part 2)
11/10Dimensionality Reduction (Part 1)
11/15Dimensionality Reduction (Part 2)
11/17Clustering (Part 1)HW 5 Due
HW6 Release (Dimension reduction)
11/22Clustering (Part 2)
11/29Reinforcement Learning Foundations (Part 1)
12/1Reinforcement Learning Foundations (Part 2)
12/6Policy Gradients
12/8Frontiers of ML (research discussions by members of CILVR)
12/13Frontiers of ML
12/14Capstone Project Due