Course In Machine Learning

Machine learning a subfield of computer science that gives computers the capability to learn without programming (explicitly). In today’s world, this subfield of computer science has given us self-driving cars, speech recognition, effective web searches and loads of other capabilities like a better understanding of the human genome.

However, this article is not about the above it is a set of introductory materials around ML created by Hal Daume III. The set of chapters included in this blog covers the major aspects of modern machine learning, be it supervised or unsupervised, large margin methods, probabilistic modeling, learning theory and much more.

A subset of these chapters can be used for an undergraduate course; a graduate course could cover all the articles.

Chapters

  1. Table of Contents
  2. Decision Trees
  3. Limits of Learning
  4. Geometry and Nearest Neighbors
  5. The Perceptron
  6. Practical Issues
  7. Beyond Binary Classification
  8. Linear Models
  9. Bias and Fairness
  10. Probabilistic Modeling
  11. Neural Networks
  12. Kernel Methods
  13. Learning Theory
  14. Ensemble Methods
  15. Efficient Learning
  16. Unsupervised Learning
  17. Expectational Maximization
  18. Structured Predictions
  19. Imitation Learning
  20. Code and Dataset

In the end, I would like to thank Hal Daume III and other faculty members who worked hard to create this awesome course that could benefit thousands of online learners like me.

Happy learning to all !!!

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