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Machine Learning

Course overview

Course Summary

Duration: 12 weeks, 84 hours

Project will be written after course completion

Course Description


Machine Learning is the study of how to build computer systems that learn from experience. It is a subfield of Artificial Intelligence and intersects with statistics, cognitive science, information theory, and probability theory, among others. The course will explain how to build systems that learn and adapt using real-world applications from industry and science (e.g., learning to classify astronomical objects, to predict medical diagnoses, to play chess, etc.)


The class will be self-contained (i.e., we will not assume any previous knowledge); a review session on probability and information theory will precede those chapters in need of background knowledge. Main topics include linear discriminants, neural networks, decision trees, support vector machines, unsupervised learning, reinforcement learning, etc.


  1.  Introduction to Machine Learning
  2. Probabilistic Learning
  3. Linear Discriminants
  4. Neural Networks
  5. Decision Trees
  6. Support Vector Machines
  7. Ensemble Learning
  8. Unsupervised Learning
  9. Bayesian Networks
  10. Evolutionary Learning
  11. Reinforcement Learning