Learning

[Updated 9/19/2009]

Six courses selected from the following three areas,with no more than four of the six courses coming from any one area.

1. Computational learning. For example: Machine Learning (CS 229), Structured Probabilistic Models: Principles and Techniques (CS 228), Modern Applied Statistics: Learning (Stats 315A), Modern Applied Statistics: Data Mining (Stat 315B), Information Theory (EE 376A), Approximate Dynamic Programming (MS&E 339), Multi-Agent Systems (CS 224M), Natural Language Processing (CS 224N/Ling 280).
2. Human learning. For example: Topics in Cognition and Learning (Educ 218), Learning in Formal and Informal Environments (Educ 366X), Cognitive Development (Psych 141), Language Acquisition I (Linguist 140/240), Language Acquisition II: Lexicon and Syntax in Acquisition (Ling 241), Introduction to Learning and Memory (Psych 45), Introduction to Cognitive Neuroscience (Psych 50) [if not taken for the core], Cognitive Neuroscience (Psych 202).
3. Learning environment design. For example: Collaborative Design and Research of Technology-Integrated Curriculum (Educ 124), Understanding Learning Environments (Educ 333A), Introduction to Human-Computer Interaction Design (CS 147), Child Development and New Technologies (Educ 342), Online Learning Communities (Educ 298/CS 377L), Designing Learning Spaces (Educ 303X), Web-Based Technologies in Teaching and Learning (Educ 391X).