Lectures (Video)
- 1. Introduction
- 2. Operators and operands
- 3. Common code patterns
- 4. Functions
- 5. Floating point numbers
- 6. Bisection methods
- 7. Lists and mutability
- 8. Log, linear, quadratic, exponential algorithms
- 9. Binary search, bubble and selection sorts
- 10. Divide and conquer methods
- 11. Testing and debugging
- 12. Introduction to dynamic programming
- 13. Dynamic programming
- 14. Analysis of knapsack problem
- 15. Abstract data types, classes and methods
- 16. Encapsulation, inheritance, shadowing
- 17. Computational models
- 18. Presenting simulation results
- 19. Biased random walks
- 20. Monte Carlo simulations
- 21. Curve fitting, linear regression
- 22. Normal, uniform, and exponential distributions
- 23. Stock market simulation
- 24. What do computer scientists do?
Introduction to Computer Science and Programming - Lecture 12
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Lecture 12 - Introduction to dynamic programming
More about debugging, knapsack problem, introduction to dynamic programming
Prof. Eric Grimson, Prof. John Guttag
6.00 Introduction to Computer Science and Programming, Fall 2008 (Massachusetts Institute of Technology: MIT OpenCourseWare) http://ocw.mit.edu Date accessed: 2009-09-14 License: Creative Commons BY-NC-SA |
Lecture Material
Supplementary lecture material is listed below.1. Lecture handout
2. The knapsack problem and dynamic programming: Introduction to dynamic programming on 20bits.com
