كورس MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016

  • 1. Introduction and Optimization Problems

  • 2. Optimization Problems

  • 3. Graph-theoretic Models

  • 4. Stochastic Thinking

  • 5. Random Walks

  • 6. Monte Carlo Simulation

  • 7. Confidence Intervals

  • 8. Sampling and Standard Error

  • 9. Understanding Experimental Data

  • 10. Understanding Experimental Data (cont.)

  • 11. Introduction to Machine Learning

  • 12. Clustering

  • 13. Classification

  • 14. Classification and Statistical Sins

  • 15. Statistical Sins and Wrap Up



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

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