| Name | Professor | Description | |
|---|---|---|---|
| COGS 200 - Cognitive Science Seminar | Various | Topics vary greatly by quarter, ML related topics not unusual
| |
| COGS 202 - Computational Models of Cognition | Rik Belew | This course surveys the development of symbolic and connectionist models of cognition.
| |
| COGS 241 - Statistical Inference and Data Analysis | Virginia de Sa | Statistical inference, hypothesis testing, model fitting, data analysis
| |
| CSE 250A - Principles of AI: Probabilistic Reasoning and Decision-Making | Lawrence Saul | An introductory graduate (or advanced undergraduate) course on probabilistic methods for reasoning and decision-making under uncertainty.
| |
| CSE 250b - Introduction to Machine Learning | Charles Elkan | Perceptrons, maximum likelihood, logistic regression, stochastic gradient following, cost-sensitive decision-making, expectation-maximization, log-linear models, conditional random fields, topic models. Four projects with papers, midterm and final exam. | |
| CSE 252C - Selected Topics in Vision & Learning | Serge Belongie | Object detection and recognition; visual category recognition; dataset issues; feature detection and description; object models; statistical pattern recognition based methods.
| |
| CSE 252a - Computer Vision I | David Kriegman | Introduction to computer vision - Feature detection, image segmentation, motion estimation, object recognition, and 3D shape reconstruction
| |
| CSE 252b - Computer Vision II | Serge Belongie | Structure from motion, projective geometry, interest point detection, feature matching, camera calibration
| |
| CSE 252c - Selected Topics in Vision and Learning | Serge Belongie | A graduate seminar devoted to recent research on pattern recognition and computer vision.
| |
| CSE 253 - Neural Networks | Gary Cottrell | Neural networks and statistical machine learning
| |
| CSE 254 - Machine Learning | Sanjoy Dasgupta | A graduate seminar devoted to recent research on AI learning methods and applications
| |
| CSE 256 - Statistical Natural Language Processing | Gary Cottrell | Modern statistical approaches to natural language processing
| |
| CSE 258a - Connectionist Natural Language Processing | Gary Cottrell | Connectionist models, cognitive processes and models
| |
| CSE 291 - Topics in CSE: Active Learning | Yoav Freund | Co-training, active learning, semi-supervised learning, proper experimental
design | |
| CSE 291 - Topics in CSE: Learning Theory I | Sanjoy Dasqupta | Covers a broad range of learning theory
| |
| CSE 291 - Topics in CSE: Online Learning | Yoav Freund | Online prediction methods
| |
| CSE 291 - Topics in High Dimensional Data Analysis | Lawrence Saul | An advanced graduate seminar in statistical methods for dimensionality reduction, feature selection, matrix factorization, and distance metric learning. | |
| CSE 291 - Topics in CSE: Topics in Unsupervised Learning | Sanjoy Dasqupta | Current topics in unsupervised learning
| |
| ECE 270 - Neurocomputing | Robert Hecht-Nielsen | Neurocomputing | |
| ECE 271A - Statistical Learning I | Nuno Vasconcelos | Statistical learning theory | |
| ECE 271B - Statistical Learning II | Nuno Vasconcelos | Discriminative learning theory | |
| ECE 275A - Parameter Estimation I | Kenneth Kretz-Delgado | Parameter estimation | |
| ECE 275B - Parameter Estimation II | Kenneth Kretz-Delgado | Parameter estimation | |
| ECE 287A - Convex Optimization and Applications | Gert Lanckriet | Convex Optimization and Applications | |
| LIGN 274 - Computational Psycholinguistics | Roger Levy | How humans learn, represent, comprehend, and produce language, studied from a computational perspective. | |
Last modified: Sun April 20 2008 02:43:12 PM