[100%OFF] Machine Learning 101 : Introduction to Machine Learning


Introduction to Machine Learning
Machine Learning 101 : Introduction to Machine Learning
Introductory Machine Learning course covering theory, algorithms and applications.
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:
  • What is learning?
  • Can a machine learn?
  • How to do it?
  • How to do it well?
  • Take-home lessons.
Outline of this Course;
  1. Lecture 1: The Learning Problem
  2. Lecture 2: Is Learning Feasible?
  3. Lecture 3: The Linear Model I
  4. Lecture 4: Error and Noise
  5. Lecture 5: Training versus Testing
  6. Lecture 6: Theory of Generalization
  7. Lecture 7: The VC Dimension
  8. Lecture 8: Bias-Variance Tradeoff
  9. Lecture 9: The Linear Model II
  10. Lecture 10: Neural Networks
  11. Lecture 11: Overfitting
  12. Lecture 12: Regularization
  13. Lecture 13: Validation
  14. Lecture 14: Support Vector Machines
  15. Lecture 15: Kernel Methods
  16. Lecture 16: Radial Basis Functions
  17. Lecture 17: Three Learning Principles
  18. Lecture 18: Epilogue
This course has some videos on youtube that has Creative Commen Licence (CC).
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