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Home » Applied Sciences » Computer Science » Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T) » 10. Understanding Experimental Data (cont.) (M-I-T)

10. Understanding Experimental Data (cont.) (M-I-T)

Course:Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): John Guttag
Level:Undergraduate

Introduction to Computational Thinking and Data Science, Fall 2016 (M-I-T)

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

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