Coursework
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📚 Selected Coursework at University of Arizona
I am on track to complete my degree in just 2.5 years with a 4.0 GPA and \(6\times\) Academic Highest Distinction (Dean’s list). While we have generous grading policies, I have consistently taken challenging courses. Below are some of the most important ones:
CSC 480 – Principles of Machine Learning
Linear and Non-linear models, SVM, Ensemble methods, Markov Chains, PGM, MCMC, LLMs, MDPs, Q-learning, value and policy iterations, DQN.
Linear and Non-linear models, SVM, Ensemble methods, Markov Chains, PGM, MCMC, LLMs, MDPs, Q-learning, value and policy iterations, DQN.
CSC 396 – Deep Learning with Natural Language Processing
Tokenization, GloVe, LSTM, Transformers, LLM finetuning and RAG.
Tokenization, GloVe, LSTM, Transformers, LLM finetuning and RAG.
Grade: A (101.8%)
CSC 352 – Systems Programming and UNIX
C programming with data structures and pointers, Unix tools and shell scripting.
C programming with data structures and pointers, Unix tools and shell scripting.
Grade: A (97.7%)
CSC 335 – Object-Oriented Programming & Design
Java, Agile, Design Patterns, GUI and software architecture.
Java, Agile, Design Patterns, GUI and software architecture.
CSC 296 – Artificial Intelligence
P&NP, Turing Machine, K-means, SVM, Supervised learning, bayes classifier, neural networks, CNN, GNN, PCA/TSNE analysis and applications in CV&NLP.
P&NP, Turing Machine, K-means, SVM, Supervised learning, bayes classifier, neural networks, CNN, GNN, PCA/TSNE analysis and applications in CV&NLP.
CSC 252 – Computer Organization
Assembly programming, machine organization, and processor architecture.
Assembly programming, machine organization, and processor architecture.
Grade: A (98.1%)
MATH 313 – Linear Algebra
Matrix operations, vector spaces, eigenvalues, SVD and applications (e.g. markov chains).
Matrix operations, vector spaces, eigenvalues, SVD and applications (e.g. markov chains).
📘 Self‑Taught Coursework
Outside the university curriculum, I self-studied advanced topics through online platforms like YouTube and Bilibili, skipping unengaging prerequisites to focus on what truly mattered.
Reinforcement Learning
Policy/value iteration, Q-learning, policy gradient, actor-critic, and deep RL with PyTorch. Completed full assignments from the David Silver & UCL course.
Policy/value iteration, Q-learning, policy gradient, actor-critic, and deep RL with PyTorch. Completed full assignments from the David Silver & UCL course.
Resource: UCL Course + Sutton & Barto