COGS 118BIntroduction to Machine Learning
This course, with COGS 118A, forms a rigorous introduction to machine learning. Topics in 118B include maximum likelihood estimation, Bayesian parameter estimation, clustering, principal component analysis, and some application areas. COGS 118A-B may be taken in either order.
Prerequisites
From the catalog: “BILD 62 or COGS 18 or CSE 11 or CSE 8B or CSE 11 and MATH 18 or MATH 31AH and MATH 20E and ECE 109 or ECON 120A or MAE 108 or MATH 180A or MATH 183 or MATH 186 and COGS 108 or COGS 109 or COGS 118A or COGS 188 or CSE 150A or CSE 151A or CSE 158 or CSE 158R or DSC 148 or ECE 174 or ECE 175A.”
Where it sits in degree plans
Required in (3)
- Cognitive Science with Specialization in Design and Interaction (B.S.)
- Cognitive Science with Specialization in Language and Culture (B.S.)
- Cognitive Science with Specialization in Machine Learning and Neural Computation (B.S.)
One of the options in (4)
- Cognitive Science (B.S.)
- Cognitive Science with Specialization in Clinical Aspects of Cognition (B.S.)
- Cognitive Science with Specialization in Neuroscience (B.S.)
- Cognitive and Behavioral Neuroscience (B.S.) (PC34)
Courses that list COGS 118B as a prerequisite (8)
- COGS 118A Supervised Machine Learning Algorithms
- COGS 118D Computational Modeling and Analysis of Human Behavior
- COGS 181 Neural Networks and Deep Learning
- COGS 182 Introduction to Reinforcement Learning
- COGS 183 Generative AI
- COGS 188 Artificial Intelligence Algorithms
- COGS 189 Brain Computer Interfaces
- CSE 151B Deep Learning