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

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