35:11
Is the Future of Linear Algebra.. Random?
09:10
The Most Important (and Surprising) Result from Information Theory
14:59
What happens at the Boundary of Computation?
12:59
The Boundary of Computation
10:47
Why Do Neural Networks Love the Softmax?
29:05
Policy Gradient Methods | Reinforcement Learning Part 6
21:16
Function Approximation | Reinforcement Learning Part 5
28:39
Temporal Difference Learning (including Q-Learning) | Reinforcement Learning Part 4
27:06
Monte Carlo And Off-Policy Methods | Reinforcement Learning Part 3
21:33
Bellman Equations, Dynamic Programming, Generalized Policy Iteration | Reinforcement Learning Part 2
18:19
Reinforcement Learning, by the Book
09:01
The Kelly Criterion
12:46
Importance Sampling
17:46
Factor Analysis and Probabilistic PCA
17:06
The Exponential Family (Part 2)
23:47
Gaussian Processes
15:58
The Exponential Family (Part 1)
13:24
The Principle of Maximum Entropy
14:07
The Bernstein Basis
12:01
Linear Regression in 12 minutes
17:28
The Fisher Information
10:55
How to Learn Probability Distributions
15:24
The Bias Variance Trade-Off
06:57
Jensen's Inequality
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