Introduction to Computer Science and Programming (Fall 2008 MIT)

• 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
• 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?