Computational Statistics and Statistical Computing
  • Popularity Contest
  • Getting started with Jupyter and Python
  • Python Functions
  • Numbers
  • Basic Graphics with matplotlib
  • Working with Text
  • Data
  • SQL
  • Bonus Material: The Humble For Loop
  • Bonus Material: Word count
  • Symbolic Algebra with sympy
  • Machine Learning with sklearn
  • Scalars
  • Vectors
  • Matrices
  • Sparse Matrices
  • Working with Matrices
  • Solving Linear Equations
  • Understanding the SVD
  • Linear least squares
  • Finding Roots of Equations
  • Numerical Optimization
  • Algorithms for Optimization and Root Finding for Multivariate Problems
  • Using optimization routines from scipy and statsmodels
  • Line search in gradient and Newton directions
  • Least squares optimization
  • Gradient Descent Optimizations
  • Constrained Optimization
  • Random Variables
  • Resampling and Monte Carlo Simulations
  • Markov Chains
  • Numerical Evaluation of Integrals
  • Abbreviated lecture notes
  • Probabilistic Programming
  • Using PyMC3
  • Metropolis and Gibbs Sampling
  • Hamiltonian Monte Carlo (HMC)
  • Introduction to C++
  • Just-in-time compilation (JIT)
  • Cython
  • Using pybind11
  • Native code compilation
  • Parallel Programming
  • Multi-Core Parallelism
  • Parallel Programming Example
  • Using ipyparallel
  • Introduction to Scalable Data Analytics
  • Scalable data storage and structures
  • Introduction to Spark
  • Architecture of a Spark Application
  • SparkContext
  • Resilient Distributed Datasets (RDD)
  • Actions and transforms
  • Working with key-value pairs
  • Persisting data
  • Spark DataFrames
  • Spark MLLib
  • Using Spark Efficiently
  • Spark magic
  • Using TensorFlow
  • Regression in TensorFlow
  • Using keras
  • CNN for MNIST digits
  • Sequential and Functional Interfaces
  • Lab00: Simple Python exercises
  • Lab02: Functions
  • Lab03: Intermediate Python Programs
  • Lab04: Data manipulation
  • Lab05: Numerics
  • Lab06: Topic Modeling with Latent Semantic Analysis
  • Lab07: Optimization
  • Lab08: Conjugate Gradient Descent
  • Lab09: C++
  • Lab10: Making Python faster
  • Lab11A: Parallel Processing
  • Lab11B: Spark
  • LabX1: Supplementary Practice Problems
  • Lab X2: Supplementary Practice Problems
  • Mock Exam
  • STA 663 Midterm Exams
Computational Statistics and Statistical Computing
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