Introduction to Computer Science and Programming (Fall 2008 MIT)

1 Introduction and Goals; Data Types, Operators, and Variables

10 Divide and Conquer Methods, Merge Sort, Exceptions

11 Testing and Debugging

12 Debugging, Knapsack Problem, Introduction to Dynamic Programming

13 Dynamic Programming Overlapping Subproblems, Optimal Substructure

14 Introduction to Object-oriented Programming

15 Abstract Data Types, Classes and Methods

16 Encapsulation, Inheritance, Shadowing

17 Computational Models Random Walk Simulation

18 Presenting Simulation Results, Pylab, Plotting

19 Biased Random Walks, Distributions

2 Branching, Conditionals, and Iteration

20 Monte Carlo Simulations, Estimating pi

21 Validating Simulation Results, Curve Fitting, Linear Regression

22 Normal, Uniform, and Exponential Distributions

23 Stock Market Simulation

24 Course Overview; What Do Computer Scientists Do?

3 Common Code Patterns Iterative Programs

4 Abstraction through Functions; Introduction to Recursion

5 Floating Point Numbers, Successive Refinement, Finding Roots

6 Bisection Methods, Newton/Raphson, Introduction to Lists

7 Lists and Mutability, Dictionaries, Introduction to Efficiency

8 Complexity Log, Linear, Quadratic, Exponential Algorithms

9 Binary Search, Bubble and Selection Sorts