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Electrical Engineering and Computer Science

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Fall 2016

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Undergraduate

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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

Course Info

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Electrical Engineering and Computer Science

As Taught In

Fall 2016

Level

Undergraduate

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Lecture Videos

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Lecture Notes

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Introduction to Computational Thinking and Data Science

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