19:39
09 5 Go
04:03
09 6 Summary
19:37
09 4 Chess
18:28
09 1 Introduction to Games
15:05
09 3 Tic Tac Toe
08:19
09 2 Properties of Games
10:33
08 7 Simpsons Paradox
06:47
08 6 Counterfactuals
11:56
08 5 Causal Inference
09:21
08 4 Causal Learning
09:25
08 3 Reichenbachs Principle
33:06
08 2 Correlation
07:49
08 1 Introduction to Causality
02:23
07 5 Outro
07:09
07 4 Graphical Models
12:48
07 3 Medical Example
21:31
07 2 Bayes Rule
16:42
07 1 Introduction to Probabilities
22:16
06 9 Deep Learning Demo
02:56
06 8 Deep Art
05:53
06 7 Convolutional Neural Networks
01:03
04 6 Summary
17:38
04 5 Gapminder Dataset
07:51
04 4 Histograms and Strips
31:01
04 3 Scatter Plots
12:56
04 2 Data Format
12:40
04 1 Introduction to Visualization
20:25
06 6 Deep Neural Networks
10:36
06 5 Gradient Descent
16:11
06 4 Minimizing the Training Error
14:16
06 3 How to Train a Neural Network
05:55
06 2 Simple Neural Network
05:03
06 1 What is Deep Learning
13:29
05 7 Parameter Tuning
07:47
05 6 Cross Validation
19:22
05 5 Other Classifiers
19:46
05 4 Evaluation in Python
07:01
05 3 How to Evaluate a Classifier
14:17
05 2 Nearest Neighbor Classifier
05:07
05 1 What is Machine Learning
00:46
03 7 Summary
11:52
03 6 Loading Your Own Data
17:25
03 5 Matrices and Vectors in Python
20:03
03 4 Matrices and Vectors
04:09
03 3 Linear Discriminant Analysis
06:51
03 2 Arrays
12:41
03 1 Fishers Iris Data
06:58
02 7 Recursion
17:47
02 6 Conditionals and Loops
15:34
02 5 Libraries
16:05
02 4 Variables and Functions
10:20
02 3 Data Types and Operators
15:57
02 2 Introduction to Python
11:06
02 1 History of Programming
17:42
01 3 Recent examples of AI
21:12
01 2 History of AI
41:01
01 1 What is AI
01:37:20
2023-01-25 PRML - Statistical Learning Theory
01:17:17
2023-01-23 Causality - Selection bias, Transportation and more
01:43:59
2023-01-23 PRML - MCMC
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