Home
1 to 5
1 - Introduction
2 - Definitions
3 - A Brief History of AI
4 - AI and Philosophy
5 - Books Worth Reading
6 to 9
6 - Data Types
7 - Introduction to ML Algorithms
8 - Algorithmic Objectives
9 - A Partial Taxonomy
10 to 13
10 - Difference Perspectives Types
11 - Geometry of Machine Learning
12 - Transformations
13 - Statistics of Machine Learnning
14 to 19
14 - Regression
15 - Classification
16 - Decision Tree
17 - Clustering
18 - Dimensionality Reduction
19 - Manifold Learning and Basis Functions/a>
20 to 23
20 - Graphs and Networks
21 - Natural Language Processing
22 - Causal Inference
23 - Timeseries Analysis
24 to 31
24 - Introduction to Artificial Neural Networks
25 - Building your first ANN
26 - Multilayer Perceptrons
27 - Deep Belief Neural Networks
28 - Introduction to Biological Vision
29 - Convolutional Deep Belief Networks
30 - Visalizing cDBNNs
31 to 37
31 - Generative AI
32 - Reinformcent Learning
33 - Agent-Based Modeling
34 - Genetic and Evolutionary Algorithms
35 - Quantum Computing and ML
36 - Ethics and Bias in AI
37 - Epiloque
