Graduate Courses

CMPSC 291I - Interactive and Real-Time User Experience of AI

Explores the design of real-time AI systems that interact with users. Covers usability, interactivity, and challenges in deploying such systems.
Grade Achieved: A+

CMPSC 291A - Neural Information Retrieval

Focuses on advanced methods for neural-based information retrieval, including vector representations, ranking models, and practical applications.
Grade Achieved: A

PSTAT 231 - Introduction to Statistical Machine Learning

Introduces statistical methods in machine learning with a focus on probabilistic modeling, Bayesian approaches, regression, classification, and dimensionality reduction.
Grade Achieved: B+ (Missed a quiz during conference trip to Spain)

CMPSC 292F - Graphs and Graph Neural Networks

Covers graph neural network theory and applications, including graph representation learning, graph convolutional networks, and their use in social networks, biology, and other fields.
Grade Achieved: TBD

CMPSC 291A - Bionic Vision

Explores AI solutions for vision-related challenges, with a focus on biomedical applications like visual prosthetics. Topics include neural encoding, sensory processing, and engineering aspects of vision systems.
Grade Achieved: TBD

Undergraduate Courses

CMPM 146 - Game AI

Covers AI algorithms for video games: search, control, and learning, and the application of AI to improve game design, development, and game play.
Grade Achieved: A+

CSE 142 - Machine Learning

Explores supervised and unsupervised learning, model evaluation, and optimization. Topics include classification, regression, and an introduction to neural networks.
Grade Achieved: A+

CSE 144 - Applied Machine Learning: Deep Learning

Focuses on deep learning methods with emphasis on neural network architectures, training techniques, and frameworks like TensorFlow and PyTorch.
Grade Achieved: A