STA663-2020
Contents:
Text
Numbers
Introduction to
pandas
Graphics and Visualization in Python
Functional programming in Python (operator, functional, itertoools, toolz)
Statistical Computing
Scalars
Vectors
Matrices
Sparse Matrices
Working with Matrices
Solving Linear Equations
Linear least squares
Applications of Linear Alebra: PCA
Understanding the SVD
Linear Algebra Examples
Review of linear algebra
Finding Roots of Equations
Numerical Optimization
Univariate Optimization
Convexity
Line Search Methods
Steepest Descent
Newton’s Method
Coordinate Descent
Solvers
GLM Estimation and IRLS
Using optimization routines from
scipy
and
statsmodels
Line search in gradient and Newton directions
Least squares optimization
Gradient Descent Optimizations
Constrained Optimization
Parallel Programming
Multi-Core Parallelism
Using
ipyparallel
Native code compilation
Just-in-time compilation (JIT)
Cython
Introduction to C++
Using
pybind11
Random Variables
Probabilistic Programming Concepts
Monte Carlo Methods
Monte Carlo integration
Markov Chains
Metropolis and Gibbs Sampling
Hamiltonian Monte Carlo (HMC)
Using PyMC3
Linear regression
Using the GLM module
Robust linear regression
Logistic regression
Hierarchical model
Mixture models
Gaussian process models
PyStan
Tensorflow
STA663-2020
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Index
Index