Graduate Courses
CMPSC 291I - Special Topics in Computer Science: Visual Computing and Interaction
Examines upcoming user interface technologies including immersive technologies (AR/VR), physiological interfaces, wearable computing, multimodal interactions, and haptics. Includes a group project on human-AI interaction techniques in AR.
Professor: Misha Sra
Grade Achieved: A+
CMPSC 291A - Neural Information Retrieval
Covers information retrieval systems and neural ranking algorithms, with emphasis on retrieval, ranking, indexing, and large-scale search services. Reviews recent research on relevance, efficiency, and scalability.
Professor: Tao Yang
Grade Achieved: A
CMPSC 291A - Special Topics in Foundation Models
Graduate research course on foundation models, with a focus on Large Language Models (LLMs). Emphasizes reading and presenting recent papers and completing a substantial course project.
Professor: Xifeng Yan
Grade Achieved: A+
PSTAT 231 - Introduction to Statistical Machine Learning
Statistical machine learning for discovering patterns in large data sets. Topics include data exploration, classification and regression trees, random forests, clustering, association rules, model selection, and performance evaluation.
Professor: Laura Baracaldo Lancheros
Grade Achieved: B+ (Missed work during conference trip to Spain)
PSTAT 235 - Big Data Analytics
Covers distributed data storage, retrieval, processing, and cloud computing, with big data methods from high-dimensional statistics and machine learning. Topics include penalized regression, classification/clustering, dimension reduction, and random projections.
Professor: Sang-Yun Oh
Grade Achieved: A
CMPSC 292F - Graphs and Graph Neural Networks
Examines graphs and graph neural networks from the perspectives of representation, reasoning, robustness, and symmetry. Topics include random walks, kernels, spectral methods, generative models, embeddings, and GNNs.
Professor: Ambuj Singh
Grade Achieved: A
CMPSC 291A - Special Topics in Computer Science: Applications
Graduate-level special topics course exploring current research areas in computer science applications. Course content varies by instructor and quarter.
Professor: Michael Beyeler
Grade Achieved: A+
Undergraduate Courses
CSE 142 - Machine Learning
Introduction to machine learning algorithms and applications. Topics include classification, density estimation, Bayesian learning regression, and online learning, with coverage of standard methods such as neural networks, decision trees, boosting, and nearest neighbor techniques.
Grade Achieved: A+
CSE 144 - Applied Machine Learning: Deep Learning
Practical, project-oriented deep learning course covering loss functions, gradient descent, logistic regression, deep neural networks, convolutional networks, and recurrent networks (including LSTMs).
Grade Achieved: A
STAT 131 - Introduction to Probability Theory
Probability theory including sample spaces, combinatorics, conditional probability, random variables, discrete and continuous distributions, expectation, variance, and limit theorems.
Grade Achieved: A
AM 10 - Mathematical Methods for Engineers I
Mathematical methods relevant to engineering, including complex numbers and exponentials, differential equations, vector spaces, and Fourier series, with emphasis on linear algebra and Fourier series.
Grade Achieved: A+
CMPM 146 - Game AI
AI in games, covering core techniques for search, control, and learning, and their application to game design, development, and game play.
Grade Achieved: A+