STA 663 (2019)

Learning objectives

• Develop fluency in Python for scientific computing
• Explain how common statistical algorithms work
• Construct models using probabilistic programming
• Implement, test, optimize, and package a statistical algorithm

Grading

• Homework 40%
• Midterm 1 15%
• Midterm 2 15%
• Project 30%

Point range for letter grade

• A 94 - 100
• B 85 - 93
• C 70 - 85
• D Below 70

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Develop fluency in Python for scientific computing

Jupyter and Python

• Introduction to Jupyter
• Using Markdown
• Magic functions
• REPL
• Data types
• Operators
• Collections
• Functions and methods
• Control flow
• Packages and namespace
• Coding style
• Understanding error messages
• Getting help
• Saving and exporting Jupyter notebooks

Text (string, re)

• The string package
• String methods
• Regular expressions
• Loading and saving text files
• Context managers
• Dealing with encoding errors

Numerics

• Issues with floating point numbers
• The math package
• Constructing numpy arrays
• Indexing
• Splitting and merging arrays
• Universal functions - transforms and reductions
• Broadcasting rules
• Sparse matrices with scipy.sparse

Data manipulation

• Series and DataFrames
• Creating, loading and saving DataFrames
• Basic information
• Indexing
• Method chaining
• Selecting rows and columns
• Transformations
• Aggregate functions
• Split-apply-combine
• Window functions
• Hierarchical indexing
• Piping with dfply

Graphics

• Graphics from the group up with matplotlib
• Statistical visualizations with seaborn
• Grammar of graphics with altair
• Building dashboards with dash

Functional programming in Python (operator, functional, itertoools, toolz)

• Writing a custom function
• Pure functions
• Anonymous functions
• Lazy evaluation
• Higher-order functions
• Decorators
• Partial application
• Using operator
• Using functional
• Using itertools
• Pipelines with toolz

Midterm 1 (15%) 01 Feb

Explain how common statistical algorithms work

Data structures, algorithms and complexity

• Sequence and mapping containers
• Using collections
• Sorting
• Priority queues
• Working with recursive algorithms
• Tabling and dynamic programing
• Time and space complexity
• Measuring time
• Measuring space

Solving linear equations

• Solving $Ax = b$
• Gaussian elimination and LR decomposition
• Symmetric matrices and Cholesky decomposition
• Geometry of the normal equations
• Gradient descent to solve linear equations
• Using scipy.linalg

Singular Value Decomposition

• Change of basis
• Spectral decomposition
• Geometry of spectral decomposition
• The four fundamental subspaces of linear algebra
• The SVD
• Geometry of spectral decomposition
• SVD and low rank approximation
• Using scipy.linalg

Optimization I

• Root finding
• Univariate optimization
• Geometry and calculus of optimization
• Gradient descent
• Batch, mini-batch and stochastic variants
• Improving gradient descent
• Root finding and univariate optimization with scipy.optim

Optimization II

• Nelder-Mead (Zeroth order method)
• Line search methods
• Trust region methods
• IRLS
• Lagrange multipliers, KKT and constrained optimization
• Multivariate optimization with scipy.optim

Dimension reduction

• Matrix factorization - PCA and SVD, MMF
• Optimization methods - MDS and t-SNE
• Using sklearn.decomposition and sklearn.manifold

Interpolation

• Polynomial
• Spline
• Gaussian process
• Using scipy.interpolate

Clustering

• Partitioning (k-means)
• Hierarchical (agglomerative Hierarchical Clustering)
• Density based (dbscan, mean-shift)
• Model based (GMM)
• Self-organizing maps
• Cluster initialization
• Cluster evaluation
• Cluster alignment (Munkres)
• Using skearn.cluster

Midterm 2 (15%) 01 March 2019

Construct models using probabilistic programming

Probability and random processes

• Working with probability distributions
• Using random
• Using np.random
• Using scipy.statistics
• Simulations

Monte Carlo methods

• Sampling from data
• Bootstrap
• Permutation resampling
• Sampling from distributions
• Rejection sampling
• Importance sampling
• Monte Carlo integration
• Density estimation

MCMC

• Bayes theorem and integration
• Numerical integration (quadrature)
• MCMC concepts
• Makrov chains
• Metropolis-Hastings random walk
• Gibbs sampler

Hamiltonian Monte Carlo

• Hamiltonian systems
• Integration of Hamiltonian system dynamics
• Energy and probability distributions
• HMC
• NUTS

Probabilistic programming

• Domain-specific languages
• Multi-level Bayesian models
• Using daft to draw plate diagrams
• Using pymc
• Using pystan

Using tesnorflow.probability

• TensorFlow basics
• Distributions and transformations
• Building probabilistic models with Edward2

Implement, test, optimize, and package a statistical algorithm

Testing

• Why test?
• Test-driven development
• Using doctest as documentation
• Using pytest to run unit tests
• Using hypothesis to auto-generate test cases
• Functional and integration testing
• Always add test if error found

Packaging and distribution

• Python modules
• Organization of a module
• Writing the setup script
• The Python Package Index
• Package managers
• Containers

Code optimization I

• Data structures and algorithms
• Vectorization
• JIT compilation with numba
• AOT compilation with cython

Code optimization II

• Interpreters and compilers
• Review of C++
• Wrapping C++ functions with pybind11

Parallel programming

• Parallel, concurrent, asynchronous, distributed
• Threads and processes
• Shared memory programming pitfalls: deadlock and race conditions
• Embarrassingly parallel programs with concurrent.futures and multiprocessing
• Map-reduce
• Master-worker
• Using ipyparallel for interactive parallelization

Final Project (30%)