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 14
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Lecture 14 - Analysis of knapsack problem
Analysis of knapsack problem, introduction to object-oriented 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. Chapter 12 of How to Think Like a Computer Scientist
3. Chapter 13 of How to Think Like a Computer Scientist
4. Chapter 14 of How to Think Like a Computer Scientist
5. Chapter 15 of How to Think Like a Computer Scientist
6. Chapter 16 of How to Think Like a Computer Scientist
