A Course in Machine Learning
豆瓣
Hal Daumé III
簡介
Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential consumer of machine learning.
CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.
目錄
0. Front Matter
1. Decision Trees
2. Geometry and Nearest Neighbors
3. The Perceptron
4. Machine Learning in Practice
5. Beyond Binary Classification
6. Linear Models
7. Probabilistic Modeling
8. Neural Networks
9. Kernel Methods
10. Learning Theory
11. Ensemble Methods
12. Efficient Learning
13. Unsupervised Learning
14. Expectation Maximization
15. Semi-Supervised Learning
16. Graphical Models
17. Online Learning
18. Structured Learning
19. Bayesian Learning
20. Back Matter