Introduction and Optimization Problems

Optimization Problems

Graph-theoretic Models

Stochastic Thinking

Random Walks

Monte Carlo Simulation

Confidence Intervals

Sampling and Standard Error

Understanding Experimental Data

Understanding Experimental Data (cont.)

Introduction to Machine Learning

Clustering

Classification

Classification and Statistical Sins

Statistical Sins and Wrap Up