Schedule

Homework
The "homework" column provides a link to the homework assignment due on each day.

Lecture
The "lecture" column identifies the lecture/discussion topic for each day.

Videos
The "videos" column identifies the instructional videos to be watched each day. The Probability Primer series provides a review of probability, and the rest of the material is in the Machine Learning series.

Readings (optional)
The "readings" column identifies supplementary material on the same topic as the videos for that day. The readings are not required, but will be useful for reference. "Murphy" refers to Machine Learning: A Probabilistic Perspective by Prof. Kevin Murphy, and "HTF" refers to The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman 2009 (5th printing), which can be downloaded for free.

Day Date Homework Lecture Videos Readings (optional)
M Jul 18 Introduction Introduction (ML 1) (optional) Introduction (Murphy 1.1-1.2.4)
T Jul 19
Probability review Probability review (PP 1.S, 2)
W Jul 20
Probability review Probability review (PP 3, 4) Probability (Murphy 2.3-2.7, 33)
R Jul 21 kNN Probability review Trees (ML 2.1-2.5)
Probability review (PP 5)		 Trees (Murphy 1.2.3, 18.3, or HTF 9.2)
F Jul 22 Probability Trees Decision theory (ML 3) Decision theory (Murphy 8.1-8.2)
S Jul 23



S Jul 24



M Jul 25 Trees Decision theory MLE (ML 4) MLE (Murphy 3.1-3.4)
T Jul 26 Decision theory MLE and MAP MAP (ML 6) MAP (Murphy 1.2.2, 4.11)
W Jul 27 MLE and MAP Bayesian statistics Bayesian statistics (ML 7) Bayesian statistics (Murphy 4.1, 4.2, 4.5, 4.6-4.9)
R Jul 28
Multivariate Gaussian Naïve Bayes (ML 8) Naïve Bayes (Murphy 1.4.3, 3.5, 4.5.3)
F Jul 29
Naïve Bayes Linear regression (ML 9) Linear regression (Murphy 1.3.1, 3.7)
S Jul 30

Multivariate Gaussian (PP 6) Multivariate Gaussian (Murphy 5.1-5.3)
S Jul 31

Bayesian linear regression (ML 10) Multivariate Gaussian (Murphy 5.4-5.5)
M Aug 01 Naïve Bayes Linear regression Estimators (ML 11) Estimators (Murphy 9.6)
T Aug 02 Linear regression
Bayesian linear regression		 Estimators Model selection (ML 12) Model selection (Murphy 1.8.5)
W Aug 03 Estimators Model selection Graphical models (ML 13.1-4, 13.8-11) Graphical models (Murphy 6.1-6.2, 6.6-6.8)
R Aug 04 Model selection Graphical models HMMs (ML 14.1-6, 14.11-12) HMMs (Murphy 6.8.4, 22.2)
F Aug 05 Graphical models HMMs K-means, GMM (ML 16) K-means (Murphy 19.2.1)
GMM (Murphy 10.4.2)
S Aug 06

EM (ML 16) EM (Murphy 10.4)
S Aug 07



M Aug 08 HMMs K-means, EM, GMM Sampling (ML 17.1-7) Sampling (Murphy 12.1-12.4)
T Aug 09 K-means and EM-GMM Sampling MCMC (ML 18.1-7, 18.9) MCMC (Murphy 12.5-12.7, 12.9)
W Aug 10 Sampling MCMC Gaussian processes (ML 19.1-5, 19.9-11) Gaussian processes (Murphy 16)
R Aug 11 MCMC Gaussian processes

F Aug 12 Gaussian processes SSL (no quiz)