核心课程
· STAT 201A Introduction to Probability at an Advanced Level
Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables.
· STAT 201B Introduction to Statistics at an Advanced Level
Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
· STAT 230 Modern applied statistics
选修课
Intro to Computer Vision and Computational Photography
Statistical Learning Theory
Randomness and Computation
Machine Learning
Designing, Visualizing and Understanding Deep Neural Networks
Urban Informatics and Visualization
Optimization Models in Engineering
Convex Optimization and Approximation
Hierarchical Statistical Models in EnvSci2
Applied Data Science with Venture Applications
Mathematical Programming (optimization)
Applied Stochastic Processes
Learning and Optimization
Applications in Data Analysis
Applied Machine Learning
Data Mining and Analytics
Applied Natural Language Processing
Natural Language Processing
Machine Learning in Education
Advanced Matrix Computation
Fundamentals of Business
Statistical Analysis of Categorical Data
Causal Inference 1
Stochastic Processes
Sampling Surveys
Introduction to Time Series
Modern Statistical Prediction and Machine Learning
The Design and Analysis of Experiments
Experimental Design
Analysis of Time Series
Special Topics
Statistical Models: Theory and Application
Causal Inference
