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Labs for Foundations of Applied Mathematics Volume III: Modeling with Uncertainty and Data

Labs for Foundations of Applied Mathematics Volume III: Modeling with Uncertainty and Data

Labs for Foundations of Applied Mathematics Volume III: Modeling with Uncertainty and Data

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Book Details:

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Global
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Category
Year:2016
Publisher:Autoedición
Pages:239 pages
Language:english
Since:08/07/2016
Size:2.11 MB
License:CC-BY

Content:

This lab manual is designed to accompany the textbook Foundations of Applied Mathematics by Humpherys and Jarvis.

Contents:

  • SQL
  • Intro to pandas
  • State Machines
  • Web Scraping
  • Web Technologies
  • Introduction to Parallel Programming
  • Collective Communication in Parallel Programs
  • MongoDB
  • Principal Component Analysis
  • Latent Semantic Indexing
  • Kalman Filter
  • Projectile Tracking
  • HMM State Estimation
  • English Language
  • HMM Parameter Estimation
  • Russian Alphabet
  • CDHMM Generation
  • Speech Recognition
  • Bayesian Updates
  • Bayesian Search
  • Metropolis Algorithm
  • Ising Model
  • Gibbs Sampling
  • Latent Dirichlet Allocation
  • K-Means Clustering
  • Gaussian Mixture Models
  • Crime Mapping
  • Classification Trees
  • Random Forests
  • K-Nearest Neighbors and Support Vector Machines
  • Image Recognition Tasks

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