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

• 1. Introduction, Optimization Problems (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)
• 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)