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Pages
Weitong Zhang
About me
Posts
publications
talks
Introduction to Graph Neural Networks and Diffusion Models
Published:
Introduction to Graph Neural Networks and Diffusion Models
Uncertainty-Aware Unsupervised and Robust Reinforcement Learning
Published:
Uncertainty-Aware Unsupervised and Robust Reinforcement Learning
From Bayesian Optimization to Bandits and RL
Published:
From Bayesian Optimization to Bandits and RL
teaching
Introductory Digital Design Laboratory
CS M152A, Lab 1, 2021 Spring, UCLA, Department of Computer Science
Introductory Digital Design Laboratory
CS M152A, Lab 3, 2021 Fall, UCLA, Department of Computer Science
Fundamentals of Artificial Intelligence
CS 161, Dis 1C, 2022 Winter, UCLA, Department of Computer Science
Fundamentals of Artificial Intelligence
CS 161, Dis 1C, 2023 Winter, UCLA, Department of Computer Science
Decision Making Systems and Reinforcement Learning
DATA 890, 2025 Spring, UNC-CH, School of Data Science and Society
Overview
This graduate-level course is designed for students with interests in machine learning, artificial intelligence, and statistical methodologies. Advanced undergraduate students are also encouraged to enroll. Sequential decision-making systems, especially those powered by reinforcement learning, are essential for the development of autonomous AI systems and a core application of modern machine learning.The course covers foundational theories and concepts in decision-making algorithms, with a focus on reinforcement learning (RL) techniques. Key topics include the principles of Markov Decision Processes (MDPs), Q-learning, and policy-based algorithms, along with hands-on analysis and exploration of their applications.