Machine Learning

An introductory course in ML covering the following topics:

  1. Introduction to different paradigms of machine learning 
  2. Linear prediction, Regression 
  3. Linear Classification, Logistic Regression, Naïve Bayes 
  4. Support Vector Machines
  5. Unsupervised Learning, Clustering, k-means 
  6. Kernel methods
  7. Neural Networks, Backpropagation 
  8. Convolutional Neural Networks 
  9. Dimensionality Reduction, PCA 
  10. Basics of optimisation