Courses
A collection of courses I've taken in ML, Math, and CS—now properly cross-listed.
Foundation Models
Graduate-level ML on state-of-the-art: transformers, scaling laws, efficient training, alignment, multimodal & diffusion models.
Theoretical Foundations of Large Scale ML
Theory & practice: optimization, generalization, modern architectures, adversarial attacks.
Matrix Methods in ML
Linear algebra foundations: classification, clustering, denoising, and neural network applications.
Artificial Intelligence
Knowledge-based search & ML: neural networks, reinforcement learning, NLP.
Bioinformatics
Algorithms for molecular biology: sequencing, alignments, phylogenetics, gene expression.
Operating Systems
Process management, concurrency & synchronization, scheduling, virtual memory, file systems, and virtualization.
Linear Algebra II
Diagonalization, Jordan form, inner product spaces, operators, bilinear forms, matrix norms.
Real Analysis
Sequences, limits, continuity, differentiation, integration, series of functions.
Linear Optimization
Proofs and theory behind the simplex method and duality.
Stochastic Processes
Discrete & continuous-time processes: queuing, branching models, Markov chains.
Proof-Based Probability
Distributions, expectation & variance, multivariate, Markov’s & Chebyshev’s inequalities, LLN, CLT.
Proof-Based Multivariable Calculus
Partial derivatives, multiple integrals, line & surface integrals.
Discrete Math
Logic, sets & relations, induction, invariants, algorithm analysis, recurrences, asymptotics.
Introduction to Big Data Systems
Docker deployment, networking, SQL, HDFS, Spark, Cassandra, Kafka, BigQuery, cloud infra.
Data Structures & Algorithms III
Version control, self-balancing trees, unit testing, GUIs, HTML, JavaScript.
Data Science II
Pandas, Matplotlib, search algorithms, web scraping, OOP, basic ML.