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Index
PREFACE Chapter 1 Introduction To Machine Learning
What is learning? When is machine learning important? Applications of machine learning
Chapter 2 Introduction to Statistics and Probability Theory
Random variables Distributions Mean, variance, and standard deviation in statistical distribution Marginalization Bayes’ theorem
Chapter 3 Building Blocks of Machine Learning
Formal statistical learning frameworks Empirical risk PAC learning strategies Generalization models for machine learning
Chapter 4 Basic Machine-Learning Algorithms
Challenges of machine learning Types of learning
Chapter 5 Supervised Machine-Learning Algorithms
Decision trees Random forest KNN algorithm Regression algorithms
Chapter 6 Unsupervised Machine Learning Algorithms
Clustering algorithm Markov algorithm Neural networks
Chapter 7 Reinforcement Machine Learning Algorithms
Q-learning SARSA
CONCLUSION FURTHER RESOURCES ABOUT THE AUTHOR
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