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Two Good e-Books

As I was wondering about a discipline to follow, me mentor gave me a link to two useful books. Take a look at them while they are free to download:

Computer Vision: Models, Learning and Inference

Part I: Probability

1. Introduction to probability
2. Probability distributions
3. Fitting probability distributions
4. The multivariate normal

Part II: Machine learning for machine vision
5. Learning and inference
6. Complex probability densities
7. Regression models for vision
8. Classification models for vision
Part III: Connecting local models
9. Graphical models
10. Directed models for images
11. Markov random fields
Part IV: Preprocessing
12. Preprocessing methods
Part V: Models for geometry
13. Pinhole camera model
14. Transformation models
15. Multiple cameras
Part VI: Computer vision models
16. Models for shape
17. Models for identity and style
18. Temporal models
19. Models for visual words
Part VI Appendices
A. Optimization
B. Image preprocessing and feature extraction
C. Linear algebra
D. Algorithms

Bayesian Reasoning and Machine Learning

Part I: Inference in Probabilistic Models

1: Probabilistic Reasoning
2: Basic Graph Concepts
3: Belief Networks
4: Graphical Models
5: Efficient Inference in Trees
6: The Junction Tree Algorithm
7: Making Decisions

Part II: Learning in Probabilistic Models

8: Statistics for Machine Learning
9: Learning as Inference
10: Naive Bayes
11: Learning with Hidden Variables
12: Bayesian Model Selection

Part III: Machine Learning

13: Machine Learning Concepts
14: Nearest Neighbour Classi cation
15: Unsupervised Linear Dimension Reduction
16: Supervised Linear Dimension Reduction
17: Linear Models
18: Bayesian Linear Models
19: Gaussian Processes
20: Mixture Models
21: Latent Linear Models
22: Latent Ability Models

Part IV: Dynamical Models

23: Discrete-State Markov Models
24: Continuous-State Markov Models
25: Switching Linear Dynamical Systems
26: Distributed Computation

Part V: Approximate Inference

27: Sampling
28: Deterministic Approximate Inference

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