Course description

This is a graduate level course on artificial intelligence (AI). This course is targeted at graduate students who want to learn about and perform current-day research in artificial intelligence and will focused on probabilistic AI. Topics covered will include state-based problem, probability theory, representations of uncertainty,  Bayesian networks, and basic principles of machine learning, Pointers to real-world applications in areas such as activity recognition, computer vision, robotics, etc., will be used as appropriate to illustrate various concepts. The course will cover the ideas underlying these applications, their implementation, and how to use them or extend them in your research.

Administrative info

Instructor

Tanzeem Choudhury | 210 Sudikoff | office hours: by appointment

Lectures

Class MWF 10:00-11:05 | x-hour (occasionally will be used to make up cancelled classes) Th 12-12:50
214 Sudikoff

Textbook

Artificial Intelligence: A Modern Approach (3rd Edition) - by Stuart Russell and Peter Norvig

Optional book

The Quest for Artificial Intelligence - by Nils J. Nilsson

Coursework and grading

Paper presentation and critique (30% total)

Done individually

Class participation (10% total)

Done individually or with a partner

Final project (60% total)

Done individually or with a partner

Grades will be assessed according to correctness and quality of the written solutions and the code you generate. For exceptionally creative and interesting work, it is possible to receive extra credit points. Extra credit is always optional, and not doing any extra credit work will never reduce your final grade, even if everyone else in the class does a lot of extra credit. 

Honor code

Dartmouth's honor code applies to this course, and academic misconduct policies will be strictly enforced. If you have questions, ask!

You must reference all sources of help and collaboration. As participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. You can use external software for your project. You should clearly report the use of external code and include pointers to such software in your project write-up. If you make use of any code taken from outside references — for instance, from an off-site web page or a textbook you must clearly attribute the source of the code with clear comments in the code that you submit.

Disabilities

Students with disabilities enrolled in this course and who may need disability-related classroom accommodations are encouraged to make an appointment to see the instructor before the end of the second week of the term. All discussions will remain confidential, although the Student Accessibility Services office may be consulted to discuss appropriate implementation of any accommodation requested.