1. Learning Paradigms
1.1. Supervised
1.1.1. Regression
1.1.1.1. Learning Algorithm
1.1.1.1.1. Non Parametric
1.1.1.1.2. Parametric
1.1.2. Classification
1.1.2.1. Fitting Models
1.1.2.1.1. Linear Regression
1.1.2.1.2. Logistic Regression
1.1.2.2. Multi i/p case
1.1.2.2.1. 2- dimension
1.1.2.2.2. 3- dimension
1.1.2.2.3. ...
1.1.2.2.4. infinite dimension
1.2. Unsupervised
1.2.1. Clustring
1.2.1.1. examples
1.2.1.1.1. Image Processing
1.2.1.1.2. Organizing computer Clusters
1.2.1.1.3. Social Network Analysis
1.2.1.1.4. Market Segmentation
1.2.1.1.5. Astronomical data analysis
1.2.1.1.6. Understanding genes data
1.3. ReInforcement
1.3.1. Applied to
1.3.1.1. Web-Crawling
1.3.1.2. Robotics
1.3.1.2.1. teaching Robot to get over obstcales
1.3.1.2.2. teaching Car drive off road and avoiding obstacles
1.3.1.2.3. robotic snake
1.3.1.2.4. 4-legged robotic dog
1.3.2. idea
1.3.2.1. Reward function
2. Learning Theory
3. Definition
3.1. 1. By Arther Samuel
3.2. 2. By Tom Mitchell
3.3. 3.
4. Applications
4.1. Natural Langauge Processing
4.2. Syntactic Pattern Recognition
4.3. Search Engines
4.4. Medical Diagonsis
4.5. Detection Credit Card fraud
4.6. Stock Market Analysis
4.7. Classifying DNA Sequence
4.8. Speech & handwriting sequences
4.9. Object Recognition in Computer Vision
4.10. Game Playing
4.11. Robot Locomotion
5. Why M.L ?
5.1. because we need to make machines ....
5.1.1. think like humans
5.1.2. notice similarties betwen things and generate new ideas
5.1.3. learn from mistakes
5.1.4. give explanation why things went wrong
5.1.5. Solve problems difficult or impossible for human to solve
5.1.5.1. problems
5.1.5.1.1. Phenomena are changing rapidly
5.1.5.1.2. Application need to be customized for each user separately
5.1.5.1.3. No human experts
5.1.5.1.4. experts unable to explain thier experience