Lectures: on Zoom (see link on Canvas), Monday and Wednesday: 10:30am-noon, Recitation: Friday: 9:30am-11:00am
See Canvas for lecture recordings; you can also download them.

Lectures and homework dates subject to change; Midterm and final dates are not

Date Assignments Topic Readings and Worksheets Quizzes

---- Review Probability and calculus (topics) Bishop 1.1-1.4, probability intro slides, MIT Probability Open Course self-test
---- Review Linear Algebra (topics) See Resources, MLMath self-test
---- Review python, numpy, jupyter, colab See Resources

W/Sep 2 Introduction,(slides) quiz
F/Sep 4 Background: colab, pandas (slides) Worksheets: Python for ML, regression in python, array practice

M/Sep 7 No class: Labor Day
W/Sep 9 Local Learning and Decision Trees (slides) decision trees, info theory; Supplemental: Decision tree viz

Worksheets: K-NN, Decision trees, info gain

F/Sep 11 MLE/MAP and Gaussians (pdf slides) Background: Gaussians

Worksheet: MLE/MAP

quiz and regression prequiz, survey

M/Sep 14 HW 0 due Linear Regression


Bishop 3.1-3.3

Worksheet: Regression with MLE/MAP

W/Sep 16 Overfitting and regularization; Bias-Variance decomposition Worksheet: Bias/Variance quiz
F/Sep 18 Generalized Linear Regression;Questions quiz,survey

M/Sep 21 HW 1 due Regression Penalties and Gradient Descent Worksheet: Ridge Regression quiz1,quiz2
W/Sep 23 Logistic (slides) and kernel Regression (slides) Supplemental: Bishop 4.0-4.5; Local Learning: Bishop 2.5 quiz1,quiz2
F/Sep 25 Review: Kernel functions, positive definite, basis... Scale Invariance

M/Sep 28 HW 2 due Neural Nets stanford CNN course
W/Sep 30 CNNs and limitations quiz
F/Oct 2 GANs and KL-divergence Supplemental (for fun): Ian Goodfellow on GANS quiz

M/Oct 5 HW 3 due Boosting, (slides) Bishop 14.3; Supplemental: shapire tutorial quiz
W/Oct 7 Support Vector Machines (SVMs) (slides) Bishop on large margin

Worksheet: SVM

F/Oct 9 Online Learning Supplemental: Perceptron proof quiz

M/Oct 12 HW 4 due Review Session, (slides) quiz
W/Oct 14 Midterm 2018 midterm and solutions
F/Oct 16 SVD (slides) SVD wikipedia; Background: matrices, Bishop, and 3blue1brown videos quiz

M/Oct 19 PCA (slides) PCA from Bishop quiz
W/Oct 21 midterm; eigenwords
F/Oct 23 Review

M/Oct 26 Clustering Bishop Ch 9 quiz
W/Oct 28 EM and Missing Data (slides)
F/Oct 30 slides

M/Nov 2 HW 5 due Netflix(slides) Supplemental: netflix winning paper quiz
W/Nov 4 Evaluation;

Final Project and project template

data sources quiz
F/Nov 6 Project advice ; Real world ML Other ML courses, visualization

Worksheet: WS: Interpreting coefficients

M/Nov 9 HW 6 due Naive Bayes (slides), and LDA (slides) Supplemental: LDA intro NB quiz;LDA quiz
W/Nov 11 Belief Nets (slides), HMMs (slides)
F/Nov 13 Belief Nets and friends

M/Nov 16 Project proposal Recurrent neural nets (slides) talk to transformer quiz
W/Nov 18 Reinforcement Learning I (slides) Supplemental: RL-intro, SARSA
F/Nov 20 Reinforcement Learning II Supplemental: RL, MDPs, MDP-2, and MDP-3

M/Nov 23 HW 7 due RL III; DQN; alphaGo; alphaZero (slides) RL quiz
W/Nov 25 No class but you can schedule a meeting to discuss your project RL review

Supplemental: RL for starcraft

F/Nov 27 No class: Thanksgiving

M/Nov 30 Autoencoders and AutoML ICA quiz1, quiz2
W/Dec 2 Active Learning (slides) Worksheet: active learning quiz
F/Dec 4

M/Dec 7 HW 8 due Bias, Causality quiz
W/Dec 9 Project due Course Review Course Summary (slides) and audio; Supplemental: summary, tesla ML video quiz
R/Dec 10 Project presentations

?/Dec ?? Final TBD Final 2018 final and solutions