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    Section 1
      1. Introduction, 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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