Differences
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courses:fall2012:598g:topics [2012/08/14 20:11] skirshne |
courses:fall2012:598g:topics [2012/12/04 10:13] (current) skirshne |
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| - | ====== Possible Set of Topics for the Course ====== | + | ====== Set of Topics for the Course ====== |
| - | The topics listed will **not** be presented in the listed order. | + | |
| * Algorithms and Data Structures | * Algorithms and Data Structures | ||
| * Computational complexity (Oh notation) | * Computational complexity (Oh notation) | ||
| Line 15: | Line 13: | ||
| * Dynamic programming (divide-and-conquer) | * Dynamic programming (divide-and-conquer) | ||
| * Matrix algorithms | * Matrix algorithms | ||
| - | * Graph algorithms | ||
| * Basics of programming | * Basics of programming | ||
| * Compilers vs interpreters | * Compilers vs interpreters | ||
| - | * UNIX/Linux environment (Lab, used in lectures) | ||
| * Programming in Python (Lab) | * Programming in Python (Lab) | ||
| * Data visualization | * Data visualization | ||
| Line 29: | Line 25: | ||
| * Pointers and memory management | * Pointers and memory management | ||
| * Input/Output | * Input/Output | ||
| - | * Interfacing C with Python | + | * Interfacing C with R |
| * Basic non-uniform random number generation | * Basic non-uniform random number generation | ||
| * Transformation | * Transformation | ||
| * Accept/reject | * Accept/reject | ||
| - | * Monte Carlo sampling | ||
| * Monte Carlo integration | * Monte Carlo integration | ||
| - | * Metropolis-Hastings and Gibbs sampling (time permitting) | ||
| * Optimization | * Optimization | ||
| * Convexity | * Convexity | ||
| Line 51: | Line 45: | ||
| * Mixture models | * Mixture models | ||
| * Expectation-Maximization (EM) | * Expectation-Maximization (EM) | ||
| - | * Bayesian estimation | ||
| * Non-parametric density estimation | * Non-parametric density estimation | ||
| * Curse of dimensionality | * Curse of dimensionality | ||
| * k-means clustering | * k-means clustering | ||
| * Rank statistics estimation | * Rank statistics estimation | ||
| - | * Bootstrap | ||
| - | * Numerical Integration | ||
| * Hidden Markov models | * Hidden Markov models | ||
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