Courses
A collection of courses I've taken and topics I've studied in depth, covering machine learning, mathematics, and computer science.
Foundation Models
Graduate-level machine learning course on the current state-of-the-art. Covering topics such as transformers, scaling laws, efficent training, alignment, multimodal models, diffusion models, and much more.
Machine Learning & AITheoretical Foundations of Large Scale Machine Learning
Graduate-level machine learning course examining theoretical ideas and understanding how well they apply in practice. Covering topics such as optimization, generalization, modern architectures, and adversarial attacks.
Machine Learning & AIMatrix Methods in Machine Learning
Linear algebraic foundations of machine learning featuring applications of matrix methods from classification and clustering to denoising and neural networks.
Machine Learning & AIArtificial Intelligence
Broad overview of AI focused on knowledge-based search techniques and machine learning methods like neural networks, reinforcement learning, and natural language processing.
Machine Learning & AIBioinformatics
Algorithms for computational problems in molecular biology including sequencing, alignment, modeling sequences, phylogenetic trees, and gene expression analysis.
Machine Learning & AILinear Algebra II
Advanced linear algebra topics like diagonalization, Jordan form, inner product spaces, operators, bilinear forms, matrix norms.
MathematicsReal Analysis
Mathematical analysis of the real number system, sequences, limits, continuity, differentiation, integration, sequences and series of functions.
MathematicsLinear Optimization
Proving and understanding the simplex method.
MathematicsStochastic Processes
Discrete and continuous-time stochastic processes with applications to queuing, branching, and other models.
MathematicsProbability with Multivariable Calculus
Probability distributions, expectation, variance, multivariate probability, Markov's and Chebyshev's inequalities, laws of large numbers, central limit theorem.
MathematicsProof-based Multivariable Calculus
Partial derivatives, multiple integrals, line and surface integrals.
MathematicsIntroduction to Big Data Systems
Deployment (Docker), Networking, SQL Databases (MySQL), HDFS, Spark, Distributed Databases (Cassandra), Kafka, Big Query, Cloud Deployment
Computer ScienceData Structures & Algorithms III
Version control, self-balancing trees, unit testing, GUIs, HTML, JavaScript.
Computer ScienceData Science II
Pandas, Matplotlib, search algorithms, web scraping, OOP, machine learning.
Computer ScienceDiscrete Math
Logic, sets, relations, mathematical induction, invariants, algorithm analysis, recurrences, asymptotic growth.
Mathematics