Here is some relevant coursework I’ve taken at Stanford.

Machine Learning

  • Supervised Learning (LMS, Logistic Regression, Perceptron, Exponential Family)
  • Generative Learning Algorithms, GDA, Naive Bayes.
  • SVMs
  • Model Selection and Feature Selection
  • Bias Variance tradeoff, Union and Chernoff/Hoeffding bounds
  • VC dimension
  • UnSupervised Learning :- K Means, EM Mizture of Gaussians
  • Factor Analysis, PCA, ICA
  • Reinforcement learning and Control, MDP's Bellman Equation
  • Value iteration and Policy iteration
  • Q-Learning

Computer Vision

  • Computer Vision
  • Face Recognition - Viola and Jones
  • Line Fitting - Detection to Model Fitting
  • Clustering and Segmentation
  • Camera Models, Camera Calibration, Epipolar Geometry
  • Stereo and Multi-view Reconstruction
  • SIFT - Implement David Lowe's paper
  • Optical Flow and Tracking.
  • Object Recognition - Bag-of-Words models
  • Object Classification and detection, Part based models (Generative,Discriminative)
  • Human Motion Recognition

Probabilistic Graphical Models

  • Bayesian Network and Markov network Representation
  • Inference methods Exact inference (variable elimination, clique trees)
  • Approximate Inference (Belief Propogation message passing, MCMC methods)
  • MRFs, CRFs
  • Learning parameters and structure
  • Hidden Markov Models
  • Applying EM to learn a HMM
  • Human Action Recognition (Kinect datasets)
  • I was one of the TA's for the course offered by Coursera for free to the public