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
Probabilistic Modeling
Covered Bayesian inference, graphical models, EM algorithm, kernel methods, and variational inference. Worked through key derivations.
Covered Bayesian inference, graphical models, EM algorithm, kernel methods, and variational inference. Worked through key derivations.
Resource: PRML by Christopher Bishop
Language Modeling from Scratch
Data collection, tokenization, transformer implementation, GPU optimization, scaling laws, pretraining, and alignment with RLHF.
Data collection, tokenization, transformer implementation, GPU optimization, scaling laws, pretraining, and alignment with RLHF.
Resource: Stanford CS336