assignments of STAT and CS courses

• CS341 Algorithms
  • Asymptotic bounds
  • divide and conquer
  • greedy algorithms
  • dynamic programming
  • graph algorithms(DFS, BFS, shortest path, minimum spanning trees)
  • NP-completeness and its implications
  • undecidability
• CS348 Introduction to Database Management
  • explored relational database and its design
• STAT331 Applied Linear Models
  • Model the relationship between a response variable and several explanatory variables via regression models
  • Apply least squares algorithm for estimation of parameters Hypothesis testing and prediction
  • Apply Model diagnostics and improvement, algorithms for variable selection
• STAT332 Sampling and Experimental Design
  • Designing sample surveys
  • Probability sampling designs
  • Estimation with elementary designs
  • Observational and experimental studies
  • Blocking, randomization, factorial designs
  • Analysis of variance
  • Designing for comparison of groups
• STAT333 Stochastic Processes
  • generating functions, conditional probability distributions and conditional expectation
  • discrete-time Markov chains with a countable state space
  • applications including the random walk, the gambler's ruin problem, and the Galton-Watson branching process
• STAT341 Computational Statistics and Data Analysis
  • grediant descent
  • cross validation
  • Horvitz-Thompson estimate
  • bootstrap sampling
• STAT441 Statistical Learning - Classification
  • neuron networks
  • SVM
  • logistic regression
  • linear/quadratic discriminant analysis
  • principal component analysis
• STAT444 Statistical Learning - Advanced Regression
  • robust regression
  • nonparametric regression such as smoothing splines, kernels, additive models, tree-based methods, boosting and bagging
  • penalized linear regression methods such as the ridge regression, lasso, and their variants