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 15
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Lecture 15 - Abstract data types, classes and methods
Abstract data types, classes and methods
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
