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
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