Lectures: 2 sessions / week, 1.5 hours / session
Recitations: 1 session / week, 1 hour / session
This course provides a survey of reasoning, learning, and optimal decision making methodologies for creating highly autonomous systems and decision support aids. It focuses on principles, algorithms, and their application, taken from the disciplines of artificial intelligence and operations research.
Reasoning paradigms include uninformed, informed and game search, logic and deduction, constraint modeling, model-based diagnosis, planning and execution, and reasoning under uncertainty. Machine learning paradigms include expectation maximization and reinforcement learning. Optimal decision making paradigms include linear and integer programming, dynamic programming and Markov decision processes.
The graduate subject 16.413 meets with undergraduate subject 16.410, but requires more advanced programming and written assignments, including an advanced tutorial in Java.
Your grade in 16.410 or 16.413 will be determined by the following approximate weighting.
However, you must do all problem sets to pass the course; a passing grade based on the other parts may be converted to a failing grade if you do not turn in all the problem sets, where turning in a problem set means including a serious attempt to complete each problem set.