Download Introduction to Statistical Machine Learning Brochure

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Introduction to Statistical Machine Learning
Description:
Machine learning allows computers to learn and discern patterns without actually being programmed. When
Statistical techniques and machine learning are combined together they are a powerful tool for analysing
various kinds of data in many computer science/engineering areas including, image processing, speech
processing, natural language processing, robot control, as well as in fundamental sciences such as biology,
medicine, astronomy, physics, and materials.
Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers
a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I
discusses the fundamental concepts of statistics and probability that are used in describing machine
learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques;
generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics
that play essential roles in making machine learning algorithms more useful in practice. The accompanying
MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range
of data analysis tasks.
- Provides the necessary background material to understand machine learning such as statistics, probability,
linear algebra, and calculus.
- Complete coverage of the generative approach to statistical pattern recognition and the discriminative
approach to statistical machine learning.
- Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both
mathematical and practical skills in a wide range of data analysis tasks
- Discusses a wide range of applications in machine learning and statistics and provides examples drawn
from image processing, speech processing, natural language processing, robot control, as well as biology,
medicine, astronomy, physics, and materials.
Contents:
Part I: Introduction to Statistics and Probability
1. Random variables and probability distributions
2. Examples of discrete probability distributions
3. Examples of continuous probability distributions
4. Multi-dimensional probability distributions
5. Examples of multi-dimensional probability distributions
6. Random sample generation from arbitrary probability distributions
7. Probability distributions of the sum of independent random variables
8. Probability inequalities
9. Statistical inference
10. Hypothesis testing
Part II: Generative Approach to Statistical Pattern Recognition
11. Fundamentals of statistical pattern recognition
12. Criteria for developing classifiers
13. Maximum likelihood estimation
14. Theoretical properties of maximum likelihood estimation
15. Linear discriminant analysis
16. Model selection for maximum likelihood estimation
17. Maximum likelihood estimation for Gaussian mixture model
18. Bayesian inference
19. Numerical computation in Bayesian inference
20. Model selection in Bayesian inference
21. Kernel density estimation
22. Nearest neighbor density estimation
Part III: Discriminative Approach to Statistical Machine Learning
23. Fundamentals of statistical machine learning
24. Learning Models
25. Least-squares regression
26. Constrained least-squares regression
27. Sparse regression
28. Robust regression
29. Least-squares classification
30. Support vector classification
31. Ensemble classification
32. Probabilistic classification
33. Structured classification
Part IV: Further Topics
34. Outlier detection
35. Unsupervised dimensionality reduction
36. Clustering
37. Online learning
38. Semi-supervised learning
39. Supervised dimensionality reduction
40. Transfer learning
41. Multi-task learning
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