Introduction to Computational Thinking and Data Science
1. Въведение - 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 (продължение)
11. Machine learning – въведение
12. Clustering
13. Classification
14. Classification and Statistical Sins
15. Statistical Sins and Wrap Up (Заключение)