Lecture 1 cartpole game 
Lecture 2 discounting future rewards 
Lecture 3 explore vs exploit 
Lecture 4 code walkthrough 
Lecture 5 where your code goes 
Lecture 6 adding the neural net 
Lecture 7 korder MDPs 
Lecture 8 Bellman equation 
Lecture 9 implementation details 
Lecture 10 ultimate doughnut 
Lecture 11 car on hill 
Lecture 12 epsilon greedy 
Programming Assignment 1Deep Qlearning with cartpole

Lecture 13 A1 post mortem 
Lecture 14 Atari games 
Lecture 15 utility shape 
Lecture 16 chain rule 
Lecture 17 chaining models 
Lecture 18 actorcritic model 
Lecture 19 read paper 
Lecture 20 equations 
Lecture 21 more equations 
Lecture 22 actor critic 
Lecture 23 A3C 
Lecture 24 More A3C 
Lecture 25 diagramming AI 
Lecture 26 more diagramming 
Lecture 27 intro to kNN 
Lecture 28 feature scaling 
Lecture 29 visualizing kNN 

Lecture 30 ball vs. kd trees 
Lecture 31 kdtree intuition 
Lecture 32 implementing kdtrees 
Lecture 33 distance to kd node 
Lecture 34 comparing learners 
Lecture 35 more comparing 
Lecture 36 correlation 
Lecture 37 distance metrics 
Lecture 38 L^{p}Norm 
Programming Assignment 2Instancebased learning

Lecture 39 PCA 
Lecture 40 more PCA 
Lecture 41 spherical distribution 
Lecture 42 medoids 
Lecture 43 PCA with cat data 
Lecture 44 GramSchmidt 
Lecture 45 accuracy of kNN 
Lecture 46 Multidimensional scaling 
Lecture 47 More MDS 
Lecture 48 nonlinear axes 
Lecture 49 Swiss roll 
Lecture 50 knee of curve 
Lecture 51 manifold of faces 
Lecture 52 transduction 
Lecture 53 intrinsic variables 
Lecture 54 summarize MDS 
Lecture 55 summarize Isomap 
Lecture 56 summarize MVU 
Programming Assignment 3Dimensionality reduction

Lecture 57 traveling salesman 
Lecture 58 relaxation 

Lecture 59 autoassociative memory 
Lecture 60 RBMs and regularization 
Lecture 61 intro to paper 
Lecture 62 Q&A about dimred 
Lecture 63 more Q&A 
Lecture 64 equations inpaper 
Lecture 65 review for midterm 


Midterm Exam

Lecture 66 answers 15 
Lecture 67 answers 6end 

Midterm Answer Key

Lecture 68 about papers 
Lecture 69 more on GANs 

Lecture 70 pixels to torques 
Lecture 71 finish P2T 
Lecture 72 Kalman project 
Lecture 73 merging beliefs 
Lecture 74 extended Kalman filter 

Lecture 75 Kalman equations 
Lecture 76 more Kalman 
Lecture 77 extended Kalman equations 
Lecture 78 finite differencing 
Lecture 79 backprop 
Lecture 80 Assignment details 
Lecture 81 more Kalman 
Lecture 82 more Kalman 
Lecture 83 more Kalman 
Lecture 84 more Kalman 
Lecture 85 more Kalman 
Lecture 86 more Kalman 
Programming Assignment 4Extended Kalman Filter

Lecture 87 derive Bayes' law 
Lecture 88 graphical models 
Lecture 89 graphical model project 
Lecture 90 Markov chains 
Lecture 91 how GM is MC 

Lecture 92 Kalman clarifications 
Lecture 93 GM details 

Lecture 94 Gibbs sampling 
Lecture 95 Metropolis Hastings 

Lecture 96 skewed data 
Lecture 97 GM and NN 
Lecture 98 More GM 
Lecture 99 debugging GM 
Lecture 100 more debugging 

Programming Assignment 5Bayesian Graphical Models

Lecture 101 Bayes optimal classifier 
Lecture 102 uncertainty 
Lecture 103 optimal ensembles 