Introduction

1 - Foreword

2 - Definitions

3 - A Brief History of AI

4 - AI and Philosophy

5 - Books Worth Reading

Foundations

6 - Data Types

7 - Introduction to ML Algorithms

8 - Algorithmic Objectives

9 - A Partial Taxonomy

The Geometry of Machine Learning

10 - Difference Perspectives Types

11 - Geometry of Machine Learning

12 - Transformations

13 - Statistics of Machine Learnning

Learning

14 - Regression

15 - Classification

16 - Decision Tree

17 - Clustering

18 - Dimensionality Reduction

19 - Manifold Learning

Beyond y=wx

20 - Graphs and Networks

21 - Natural Language Processing

22 - Causal Inference

23 - Timeseries Analysis

Neural Networks

24 - Introduction to 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

Current and Future Directions

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