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