Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; NaΓ―ve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcem
Machine learning. An applied mathematics introduction
β Scribed by Wilmott, Paul
- Publisher
- Panda Ohana
- Year
- 2019
- Tongue
- English
- Leaves
- 242
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Contents......Page 8
Prologue......Page 12
Chapter 1 - Introduction......Page 16
Chapter 2 - General Matters......Page 32
Chapter 3 - K Nearest Neighbours......Page 70
Chapter 4 - K Means Clustering......Page 80
Chapter 5 - Naive Bayes Classifier......Page 98
Chapter 6 - Regression Methods......Page 106
Chapter 7 - Support Vector Machines......Page 114
Chapter 8 - Self-Organizing Maps......Page 128
Chapter 9 - Decision Tree......Page 142
Chapter 10 - Neural Networks......Page 162
Chapter 11 - Reinforcement Learning......Page 188
Datasets......Page 232
Epilogue......Page 236
Index......Page 238
β¦ Subjects
matematika;matematika -- strojno uΔenje
π SIMILAR VOLUMES
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