Contents
1 Introduction 5
2 Machine Learning Models 7
2.1 The big picture............................. 8
2.2 Exploratory data analysis....................... 10
2.3 Distance metrics............................ 15
2.4 Classification problems........................ 17
2.5 Majority vs Random class....................... 21
2.6 Nearest neighbour classifier ..................... 22
2.7 Classification trees .......................... 23
2.8 Choosing the best model ....................... 27
2.9 Perceptron learning .......................... 33
2.10 Clustering................................ 37
2.11 Suggested readings.......................... 40
3 Metaheuristic Methods ................... 41
3.1 The big picture............................. 42
3.2 Visual insights ............................. 44
3.3 Exhaustive search........................... 48
3.4 Random search ............................ 53
3.5 An object-oriented approach ..................... 54
3.6 Hill Climbing .............................. 59
3.7 Random Walk.............................. 70
3.8 Simulated Annealing.......................... 71
3.9 Genetic Algorithms .......................... 74
3.10 Fitness function ............................ 79
3.11Genetic operators ........................... 80
3.12 Metaheuristics benchmarks ..................... 88
3.13 Estimation of Distribution Algorithms ................ 92
3.14 Suggested readings.......................... 96