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14. Classification and Statistical Sins
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Classroom Contents
Introduction to Computational Thinking and Data Science
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- 1 1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)
- 2 2. Optimization Problems
- 3 3. Graph-theoretic Models
- 4 4. Stochastic Thinking
- 5 5. Random Walks
- 6 6. Monte Carlo Simulation
- 7 7. Confidence Intervals
- 8 8. Sampling and Standard Error
- 9 9. Understanding Experimental Data
- 10 10. Understanding Experimental Data (cont.)
- 11 11. Introduction to Machine Learning
- 12 12. Clustering
- 13 13. Classification
- 14 14. Classification and Statistical Sins
- 15 15. Statistical Sins and Wrap Up
