MIT

20 Monte Carlo Simulations, Estimating pi

Lecture 16: Using Randomness to Solve Non-random Problems (M-I-T)

21 Validating Simulation Results, Curve Fitting, Linear Regression

Lecture 17: Curve Fitting (M-I-T)

22 Normal, Uniform, and Exponential Distributions

Lecture 18: Optimization Problems and Algorithms (M-I-T)

23 Stock Market Simulation

Lecture 19: More Optimization and Clustering (M-I-T)

24 Course Overview; What Do Computer Scientists Do?

Lecture 1: Introduction to Computer Science and Programming (M-I-T)

3 Common Code Patterns Iterative Programs

4 Abstraction through Functions; Introduction to Recursion

Lecture 20: More Clustering (M-I-T)

5 Floating Point Numbers, Successive Refinement, Finding Roots

Lecture 21: Using Graphs to Model Problems, Part 1 (M-I-T)

6 Bisection Methods, Newton/Raphson, Introduction to Lists

Lecture 22: Using Graphs to Model Problems, Part 2 (M-I-T)

Lecture 23: Dynamic Programming (M-I-T)

7 Lists and Mutability, Dictionaries, Introduction to Efficiency

8 Complexity Log, Linear, Quadratic, Exponential Algorithms

Lecture 24: Avoiding Statistical Fallacies (M-I-T)

9 Binary Search, Bubble and Selection Sorts

Lecture 25: Queuing Network Models (M-I-T)

Lecture 26: What Do Computer Scientists Do? (M-I-T)

Lecture 2: Core Elements of a Program (M-I-T)