STATS202: Data Mining & Analysis

statistical regression techniques in R (principal components analysis, clustering, classification, cross validation, bootstrap, dimensionality reduction, splines, decision trees, support vector machines)

CS109: Introduction to Probability for Computer Scientists

probability theory (combinatorics, random variables, distributions, sampling, parameter estimation, machine learning [classification, naïve bayes, logistic regression])

CS107: Computer Organization and Systems

writing C programs that handle memory and pointers; understanding and improving address space, compile, and runtime behavior; translating between C and assembly; working in Unix

CS103: Mathematical Foundations of Computing

discrete mathematics (logic, proofs, graphs), computability theory (regular languages, Turing machines), complexity theory (P vs. NP)

ECON45: Using Big Data to Solve Economic and Social Problems

Stata; data analysis; experimental analysis; income, opportunity, and educational inequality

EARTH1B: Big Data for Sustainability

exploring AI / ML applications to solve challenges such as global energy needs, food and water insecurity, climate change, and natural hazards

CS279: Computational Biology: Structure and Organization of Biomolecules and Cells

computational techniques for studying the structure and dynamics of biomolecules and cells (protein structure prediction and design, molecular dynamics simulations, docking, image analysis of microscopy)