Video Lectures

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

2: Branching, Conditionals, and Iteration

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

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

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?