Machine Learning 101 : Introduction to Machine Learning

Machine Learning 101 : Introduction to Machine Learning


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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).

Instructors: Didaktik ACADEMY

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