|
Home
| Subject Search
| Help
| Symbols Help
| Pre-Reg Help
| Final Exam Schedule
| My Selections
|
Searched for: 1 subject found.
6.036 Introduction to Machine Learning
(
,
)
Prereq: Calculus II (GIR) and (6.00 or 6.01)
Units: 4-0-8
Credit cannot also be received for 6.862
Lecture: T9.30-11 (26-100) Recitation: R9.30-11 (34-501) or R11-12.30 (34-501) or R1-2.30 (34-501) or R2.30-4 (34-501) or F9.30-11 (34-501) or F11-12.30 (34-501) or F1-2.30 (34-501) or F2.30-4 (34-501) +final
Introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction; formulation of learning problems; representation, over-fitting, generalization; clustering, classification, probabilistic modeling; and methods such as support vector machines, hidden Markov models, and neural networks. Students taking graduate version complete additional assignments. Meets with 6.862 when offered concurrently. Recommended prerequisites: 6.006 and 18.06. Enrollment may be limited; no listeners.
Fall: L. Kaelbling
Spring: L. Kaelbling
No textbook information available
| ||||||||
