23:43

A Bird's Eye View of Recommender Systems

35:00

A bird's eye view of neural networks

01:34:49

Experimenting with JAX | AI Professor Improvises Chess Programming #16

02:01:11

Debugging Because Nothing is Working | AI Professor Improvises Chess Programming #15

01:38:16

Attempting Experience Replay | AI Professor Improvises Chess Programming #14

01:23:49

Random Projections | AI Professor Improvises Chess Programming #13

01:34:49

Actual Reinforcement Learning | AI Professor Improvises Chess Programming #12

01:34:36

Fixing Bugs in Fake Reinforcement Learning | AI Professor Improvises Chess Programming #11

01:16:15

Setting up for Reinforcement Learning | AI Professor Improvises Chess Programming #10

01:46:15

Fixing (?) Quiescence Search | AI Professor Improvises Chess Programming #9

01:49:20

Refactoring & Trying to Spell Quiescence Search | AI Professor Improvises Some Chess Programming #8

01:00:40

Time Management Prototype | AI Professor Improvises Some Chess Programming #7

01:24:52

Introducing Bugs and Not Fixing Them | AI Professor Improvises Some Chess Programming #6

01:10:14

Attempting Alpha-Beta Pruning | AI Professor Improvises Some Chess Programming #5

01:26:25

Pruning the Search Tree | AI Professor Improvises Some Chess Programming #4

59:05

Making Moves on Lichess, but Now Slowly! | AI Professor Improvises Some Chess Programming #3

42:37

Slight Improvements to Heuristic | AI Professor Improvises Some Chess Programming #2

45:26

Trading a Queen for a Pawn | AI Professor Improvises Some Chess Programming #1

25:19

What is soft or max about the softmax function?

28:36

Avoid Numerical EXPLOSIONS with the Log Sum Exp Trick

56:56

What Decision Makers Should Know About Machine Learning

44:39

Fairness in Machine Learning

13:48

Reduced-Bias Co-Trained Ensembles for Weakly Supervised Cyberbullying Detection

17:37

Relating the Binary and Multi-class Perceptron (handwritten notes)

15:54

Perceptrons are Weird! (handwritten notes)

18:55

Lagrangian relaxation (handwritten notes)

38:33

Back Propagation

02:45

The Worst Unboxing Video of NVIDIA Titan V

02:05

monk parakeets of barcelona

18:28

All Lagrangian Duals (of Minimizations) are Concave

02:57

Have Yourself a Merry Little Christmas

18:39

Thoughts from NeurIPS 2017

03:01

Beyond Parity: Fairness Objectives for Collaborative Filtering

16:34

Particle Filters

20:25

First Order Logic Overview

22:49

Passive Reinforcement Learning

10:07

Probability Basics

12:46

A* Optimality

26:11

Pruning (Alpha-Beta)

20:59

Adversarial search for game playing artificial intelligence

02:47

Ungrateful Baby Woodpecker

21:20

Model Complexity and VC Dimension

14:19

What is Learning Theory?

10:03

Graphical Models Wrap up

22:17

Belief Propagation

18:27

Undirected Graphical Models

30:18

Hidden Markov Models

18:32

Markov Models

30:03

17 Probabilistic Graphical Models and Bayesian Networks

06:00

15.1 GMM Degeneracy

22:00

16 Variational EM and K Means

26:56

15 Clustering and Mixture Models

33:48

14 Principal Component Analysis

23:32

11 Feature Maps and Kernels

34:47

10 Dual SVM and Kernels

23:32

11 Feature Maps and Kernels

22:19

13 Regression

17:48

12 SMO and Stochastic SVM

28:55

9 Support Vector Machines

25:41

7 Learning Objective Functions