- 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
-
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