Coursework

(Statistical) Machine Learning

UW

• STAT 538, Statistical Learning: Modeling, Prediction, and Computing
  • Convex optimization, high-dimensional inference, sparsity, LASSO, integer programming.
• CSE 541, Interactive Learning
  • Multi-armed bandits, adversial bandits, linear bandits, contextual bandits.
• CSE 547, Machine Learning for Big Data
  • MapReduce, Spark, large-scale machine learning, recommendation systems, social networks.

UC Berkeley

• DATA C8, The Foundations of Data Science
  • Basic data manipulation and visuliazation, basic probability and statitics, regression.
• DATA C100, Principles and Techniques of Data Science
  • Data cleaning, SQL, Pandas, visualization, PCA, clustering, ordinary least sqaures.
• DATA C102, Data, Inference, and Decisions
  • Multiple testing, online FDR control, Bayesian modeling, generalized linear models.
• CS 182, Designing, Visualizing and Understanding Deep Neural Networks
  • Fully connected neural networks, CNNs, RNNs, transformers, Generative models.
• CS 188, Introduction to Artificial Intelligence
  • Search algorithms, logic, Bayes net, Markov decision processes, reinforcement Learning.
• CS 189, Introduction to Machine Learning
  • Theoretical foundations, algorithms, methodologies, and applications for machine learning.

Optimization

UC Berkeley

• EECS 127, Optimization Models in Engineering
  • Matrix algebra, SVD, convex optimization, duality.
• IEOR 160, Nonlinear and Discrete Optimization
  • Optimality conditions, convex optimization, numerical algorithms, integer programming.
• IEOR 162, Linear Programming and Network Flows
  • Simplex method, optimality, duality, graph and network flow problems.

Statistics

UW

• STAT 502, Design and Analysis of Experiments
  • ANOVA, factorial treatment designs, block designs, split-plot designs.
• STAT 512, Statistical Inference
  • Probability theory, maximum likelihood estimation, asymptotic theories.
• STAT 513, Statistical Inference
  • Sufficient and complete statistics, UMVUE, hypothesis testing, decision theory.
• STAT 516, Stochastic Modeling of Scientific Data
  • Discrete-time Markov chain, Markov Chain Monte Carlo.
• STAT 517, Stochastic Modeling of Scientific Data
  • Continuous-time Markov chains, Gaussian process, Gaussian Markov random fields, point process.
• STAT 581, Advanced Theory of Statistical Inference I
  • Elementary (Bayesian) decision theory, modes of convergence, M-estimation, hypothesis testing under fixed and local alternatives, parametric efficiency
• STAT 582, Advanced Theory of Statistical Inference II
  • Minimax rates of convergence, kernel-based density estimation, concentration inequalities, entropy argument, empirical processes and weak convergence
• STAT 583, Advanced Theory of Statistical Inference III (In Progress)
  • Weak convergence, Donsker class, U-statistics, asymptotic linearity and efficiency, influence function
• STAT 570, Advanced Regression Methods for Independent Data
  • Linear models, generalized linear models, non-linear models, sandwich estimators
• STAT 571, Advanced Regression Methods for Dependent Data
  • Linear mxed models, generalized estimating equations, generalized linear mixed models, missing data, classical and multivariate analysis
• STAT 591, Multiple Testing and Modern Inference
  • FWER control, FDR control, Benjamini-Hochberg procedure, Knockoffs.

UC Berkeley

• STAT 135, Concepts of Statistics
  • Method of moments, MLE, linear regression, hypothesis testing, bootstrap.
• STAT 151A, Linear Modelling: Theory and Applications
  • Data transformation, coefficient inference, ANOVA, model diagonistics, logistic regression.
• STAT 153, Introduction to Time Series
  • Stationarity, ARIMA model, Fourier transform, spectral density.
• STAT 156, Causal Inference
  • Randomized experiments, unconfounded observational studies, sensitivity analysis, instrumental variables.
• STAT 159, Reproducible and Collaborative Statistical Data Science
  • Git, Conda, JupyterBook, Github pages.

Mathematics

UW

• STAT 559, Measure Theory
  • Measures, measurable functions, Lebesgue integration, random variables, modes of convergence.
  • My lecture notes.
• CFRM 550, Stochastic Calculus for Quantitative Finance
  • Martingales, Brownian motion, stochastic calculus, stochastic differential equation.

UC Berkeley

• MATH 53, Multivariable Calculus
  • Partial derivatives, multiple integrals, vector calculus.
• MATH 54, Linear Algebra & Differential Equations
  • Basic linear algebra, vector space, eigenvalues, ordinary differential equations.
• MATH 104, Introduction to Analysis
  • Sequence, limits, metric space, continuity, series, Riemann integral.
• MATH 110, Abstract Linear Algebra
  • Matrices, QR factorization, inner product, Jordan canonical form.
• DATA C140, Probability for Data Science
  • Random variables, multivariate normals, central limit theorem, Markov chains, regression.
• CS 70, Discrete Mathematics and Probability Theory
  • Modular arithemtic, graphs, RSA, counting, probability.

Computer Programming

UC Berkeley

• EECS 16B, Designing Information Devices and Systems II
  • Circuits, control, SVD, PCA, linearization.
• CS 61A, Structure and Interpretation of Computer Programs
  • Python, Recursion, Scheme, SQL
• CS 61B, Data Structures
  • Java, object-oriented programming, array, tree, queue, hashing, sorting, graph.
• CS 61C, Great Ideas in Computer Architecture (Machine Structures)
  • C, memory management, RISC-V, parallelism, caches, virtual memory.
• STAT 33B, Introduction to Advanced Programming in R
  • R, ggplot, Tidyverse.

Finance

Coursera

• Financial Markets (In Progress)
  • Basics of financial markets, behavioral finance, regulation, stocks, options, bond, investment banking.