Course Topics

Topics covered in this course include:

Representations and interpretation of uncertain information:

• Bayesian reasoning
• Dempster Schaefer
Graphical reasoning models:
• Bayesian networks
• Markov networks
Information fusion and prediction:
• Kalman filters
• Particle filters
Randomized and sampling-based inference techniques:
• Sampling Bias and weighted sampling
• Gibbs Sampling
• Markov Chain Monte Carlo Techniques
Probabilistic model construction:
• Hidden Markov Models
Decision-making with uncertain actions and sensors:
• Fully and Partially Observable Markov Decision Processes
• Monte-Carlo simulation techniques