COMPSCI 181 - Intelligent Machines: Perception, Learning, and Uncertainty

Institution:

Harvard University

Subject:

Description:

Introduction to artificial intelligence, focusing on problems of perception, reasoning under uncertainty, and especially machine learning. Supervised learning algorithms. Decision trees. Ensemble learning and boosting. Neural networks, multi-layer perceptrons and applications. Support vector machines and kernel methods. Clustering and unsupervised learning. Probabilistic methods, parametric and non-parametric density estimation, maximum likelihood and maximum a posteriori estimates. Bayesian networks and graphical models: representation, inference and learning. Hidden Markov models. Markov decision processes and reinforcement learning. Computational learning theory.

Credits:

4.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.harvard.edu

Phone Number:

(617) 495-1000

Regional Accreditation:

New England Association of Schools and Colleges

Calendar System:

Semester

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