Introduction to Computational Thinking and Data Science | Electrical Engineering and Computer Science | MIT OpenCourseWare

Course Info

Instructors

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2016

Level

Undergraduate

Topics

Learning Resource Types

Lecture Videos

Problem Sets

Lecture Notes

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Lecture Videos

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

Instructors

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2016

Level

Undergraduate

Topics

Learning Resource Types

Lecture Videos

Problem Sets

Lecture Notes

Programming Assignments

Download Course

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