People Innovation Excellence

Artificial Intelligence (4 Credits)

Learning Outcomes:

Represent the causal structure of a given domain using Bayesian networks and use it to make both quantitative (probabilistic) and qualitative inferences;  Given a simple version of a problem such as object recognition or text categorization, implement a bayesian network that solves the problem and explain how learning takes place in the bayesian network; Identify the steps in natural language processing, list some of the problems in understanding and generation, and describe how information retrieval, information extraction, and language translation systems work; Choose an appropriate method for robot navigation and justify its choice over other methods such as exhaustive search and exact inference; Recognize when a problem is not amenable to a traditional programming (e.g., procedural, object-oriented, etc) solution, but might be amenable to knowledge-based or learning-based methods


  1. Introduction Rational Agents
  2. Search
  3. Reasoning
  4. Probability and Bayesian Networks
  5. Statistical Learning and Spam Filtering
  6. Probabilistic Reasoning over Time
  7. Statistical Learning
  8. Natural Language Processing
  9. Computer Vision
  10. Robotics

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