Syllabus

Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 1 hour / session

Course Description

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.

Prerequisites

6.01 or 1.00.

Additional Information

Learning Objectives and Assessment

Course Policies

Readings

[AIMA] = Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd ed. Prentice Hall, 2009. ISBN: 9780136042594.

[IOR] = Hillier, Frederick, and Gerald Lieberman. Introduction to Operations Research. 9th ed. McGraw-Hill, 2009. ISBN: 9780077298340.

[PA] = LaValle, Steven. Planning Algorithms. Cambridge University Press, 2006. ISBN: 9780521862059.

[JINS] = Flanagan, David. Java in a Nutshell. 5th ed. O'Reilly, 2005. ISBN: 9780596007737.

Grades

Your grade in 16.410 or 16.413 will be determined by the following approximate weighting.

16.410

16.413

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.

1. Homework: You are expected to do all the homework. While performance on exams is an indication of basic competence, performance on homework is your major opportunity to demonstrate outstanding achievement in 16.410-13. Mediocre homework performance will result in a lower grade, even if performance on exams is good. It is virtually impossible to get an A in 16.410-13 unless all homework assignments have been turned in. Missing more than a couple of the homework assignments may result in a failing grade for the semester, regardless of performance on exams, and in class participation. This applies to the weekly problem sets and to the final project.


2. Participation in class: You are expected to participate actively in lecture discussions, and to read assigned material before class.