Electrical Engineering and Computer Science
Introduction to Computational Thinking and Data Science
Lecture 1: Introduction and Optimization Problems
Lecture 2: Optimization Problems
Lecture 3: Graph-theoretic Models
Lecture 4: Stochastic Thinking
Lecture 5: Random Walks
Lecture 6: Monte Carlo Simulation
Lecture 7: Confidence Intervals
Lecture 8: Sampling and Standard Error
Lecture 9: Understanding Experimental Data
Lecture 10: Understanding Experimental Data (cont.)
Lecture 11: Introduction to Machine Learning
Lecture 12: Clustering
Lecture 13: Classification
Lecture 14: Classification and Statistical Sins
Lecture 15: Statistical Sins and Wrap Up
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