1 Goals of the course; what is computation; introduction to data types, operators, and variables
2 Operators and operands; statements; branching, conditionals, and iteration
3 Common code patterns: iterative programs
4 Decomposition and 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, pseudocode, 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 More about debugging, knapsack problem, introduction to dynamic programming
13 Dynamic programming: overlapping subproblems, optimal substructure
14 Analysis of knapsack problem, 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; misuse of statistics
23 Stock market simulation
24 Course overview; what do computer scientists do?