Lectures and Labs (along with readings for these lectures)
https://am207.github.io/2017/lectures/
Homework
https://am207.github.io/2017/homework/
Topics Index
https://am207.github.io/2017/topics.html
Terms Glossary
https://am207.github.io/2017/terms.html
Videos. You will find there a live feed for the current lecture!
https://matterhorn.dce.harvard.edu/engage/ui/index.html#/2017/02/24932
Zoom Lab Videos
https://vimeo.com/channels/1194246
Sequentially
Week 1
Lecture 1: Introduction
https://am207.github.io/2017/lectures/lecture1.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=25c6b2b9-864a-4ead-bd3b-cf9af8b1e201
Lecture 2: Frequentist Stats
https://am207.github.io/2017/lectures/lecture2.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=9c427fac-29d5-4ef9-b7d0-fb9eb6f11a58
Lab 1: Frequentist Example
https://am207.github.io/2017/lectures/lab1.html
Part 1 ：https://vimeo.com/201321508
Part 2 ：https://vimeo.com/201322530
Week 2
Lecture 3: Law of Large Numbers, CLT, and Monte Carlo
https://am207.github.io/2017/lectures/lecture3.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=bc46d8be-32ad-4c71-88a2-b0af4d71110b
Lecture 4: Sampling Methods
https://am207.github.io/2017/lectures/lecture4.html
https://am207.github.io/2017/lectures/lecture4.html
Lab2: Stratified Sampling and Math
https://am207.github.io/2017/lectures/lab2.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=03b49382-125a-4002-9346-ff79f13ff201
Week 3
Lecture 5: Machine Learning
https://am207.github.io/2017/lectures/lecture5.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=9d0d79cc-18bd-4f07-aca9-087f492720f5
Lecture 6: Gradient Descent
https://am207.github.io/2017/lectures/lecture6.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=dd5cdda8-5810-407f-8bd0-0d20c8c89d2f
Lab3: Theano, GD, and SGD
https://am207.github.io/2017/lectures/lab3.html
https://vimeo.com/203542166
Week 4
Lecture 7: Information Theory
https://am207.github.io/2017/lectures/lecture7.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=8626c897-c1e8-48e4-89d6-f9535945654a
Lecture 8: AIC and Simulated Annealing
https://am207.github.io/2017/lectures/lecture8.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=69902285-6bab-4236-bdaf-4f7a0e52c5ee
Lab4: Simulated Annealing
https://am207.github.io/2017/lectures/lab4.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=91cecfea-3737-46ff-b9a5-a7fa4c546218
Week 5
Lecture 9: Annealing, Metropolis, Markov, and MCMC
https://am207.github.io/2017/lectures/lecture9.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=204a0c4d-6446-4455-9db0-eaa0d0c25cfb
Lecture 10: Matropolis-Hastings and Bayes, with some Discrete Sampling
https://am207.github.io/2017/lectures/lecture10.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=f6fa497d-1f25-4ad5-9c37-5c1e68552b3d
Lab5: Metropolis and Bayes
https://am207.github.io/2017/lectures/lab5.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=28967b40-8841-4459-8fa9-8dff4779601e
Week 6
Lecture 11: Bayes
https://am207.github.io/2017/lectures/lecture11.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=4f69bad3-96ff-4613-8a3d-7d4f30992b57
Lecture 12: Gibbs and Hierarchical Models
https://am207.github.io/2017/lectures/lecture12.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=840f668a-099a-4c72-8346-d36a6d7aa772
Lab 6: Tetchy gibbs and Rat Tumor Full Bayes
https://am207.github.io/2017/lectures/lab6.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=9479a00b-da07-48fd-bfa2-040f1aafe126
Week 7
Lecture 13: Bayes
https://am207.github.io/2017/lectures/lecture13.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=73c1afbc-93a2-44b7-87a5-10bb804f7c97
Lecture 14: Gibbs and Hierarchical Models
https://am207.github.io/2017/lectures/lecture14.html
https://matterhorn.dce.harvard.edu/engage/player/watch.html?id=01d02827-eee9-4e02-9a56-74f11e4b6bb2
Lab 7: Bioassay and tumors in pymc3
https://am207.github.io/2017/lectures/lab7.html
Week 8
Lecture 15: Recap and Dataq Aug to HMC
https://am207.github.io/2017/lectures/lecture15.html
Lecture 16: Recap and Slice and HMC
https://am207.github.io/2017/lectures/lecture16.html
Lab 8: pymc, da, theano, slice
https://am207.github.io/2017/lectures/lab8.html
Week 9
Lecture 17: HMC, and tetchy hierarchicals
https://am207.github.io/2017/lectures/lecture17.html
Lecture 18: HMC tuning, glm, Model checking
https://am207.github.io/2017/lectures/lecture18.html
Lab 9: Gelman Schools, homework
https://am207.github.io/2017/lectures/lab9.html
Week 10
Lecture 19: Model Checking, glms
https://am207.github.io/2017/lectures/lecture19.html
Lecture 20: Model Comparison, glms
https://am207.github.io/2017/lectures/lecture20.html
Lab 10: Prosocial Chimps Bernoulli glm
https://am207.github.io/2017/lectures/lab10.html
Week 11
Lecture 21: Utility, Model Comparison
https://am207.github.io/2017/lectures/lecture21.html
Lecture 22: x-val, Mixture Models
https://am207.github.io/2017/lectures/lecture22.html
Lab11: Semi-Supervised learning and log-sum-exp marginals
https://am207.github.io/2017/lectures/lab11.html
Week 12
Lecture 23: Expectation Maximization
https://am207.github.io/2017/lectures/lecture23.html
Lecture 24: Expectation Maximization and Variational Inference
https://am207.github.io/2017/lectures/lecture24.html
Lab12: Correlations and Mixtures and ADVI
https://am207.github.io/2017/lectures/lab12.htmll
Week 13
Lecture 25: Gaussian Processes
https://am207.github.io/2017/lectures/lecture25.html
Lecture 26: Wrapup
https://am207.github.io/2017/lectures/lecture26.html
