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This is primarily a project and presentation based course.

The basic information covered in the class will be:

• Markov Models
• Latent Semantic Indexing
• Factored Markov Models
• Hidden Markov Models
• Collaborative Filtering
• Dynamic Bayesian Networks
• Stochastic Reasoning on Markov Models