Course Topics

Topics covered in this course include:

Representations and interpretation of uncertain information:

• Bayesian reasoning
• Dempster Schaefer
Information fusion and prediction:
• Kalman filters
• Particle filters
Experimental Data Interpretation:
• Significance Testing
Probabilistic model construction:
• Hidden Markov Models
Decision-making with uncertain actions and sensors:
• Fully and Partially Observable Markov Decision Processes
• Monte-Carlo simulation techniques