Download free course Annotated Algorithms in Python, pdf file on 388 pages by by Massimo Di Pierro.
This book is assembled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University. The lectures cover multiple classes, including Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. These lectures teach the core knowledge required by any scientist interested in numerical algorithms and by students interested in computational finance.Table of contents
The algorithms you will learn can be applied to different disciplines. Throughout history, it is not uncommon that an algorithm invented by a physicist would find application in, for example, biology or finance.
Overview of the Python Language
Types of variables
Python control flow statements
How to import modules
Theory of Algorithms
Order of growth of algorithms
Types of algorithms
Artificial intelligence and machine learning
Long and infinite loops
Well-posed and stable problems
Approximations and error analysis
Sparse matrix inversion
Solvers for nonlinear equations
Optimization in one dimension
Functions of many variables
Probability and Statistics
Combinatorics and discrete random variables
Random Numbers and Distributions
Randomness, determinism, chaos and order
Parallel generators and independent sequences
Generating random numbers from a given distribution
Probability distributions for continuous random variables
Monte Carlo Simulations
Error analysis and the bootstrap method
A general purpose Monte Carlo engine
Monte Carlo integration
Stochastic, Markov, Wiener, and processes
Markov chain Monte Carlo (MCMC) and Metropolis
Master-Worker and Map-Reduce
Math Review and Notation
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