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Introduction
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Programming Lectures
Introduction
Operators and operands statements branching, conditionals, and iteration
Common code patterns: iterative programs
Decomposition and abstraction through functions introduction to recursion
Floating point numbers, successive refinement, finding roots
Bisection methods, Newton/Raphson, introduction to lists
Lists and mutability, dictionaries, pseudocode, introduction to efficiency
Complexity log, linear, quadratic, exponential algorithms
Binary search, bubble and selection sorts
Divide and conquer methods, merge sort, exceptions
Testing and debugging
More about debugging, knapsack problem, introduction to dynamic programming
Dynamic programming: overlapping subproblems, optimal substructure
Analysis of knapsack problem, introduction to object-oriented programming
Abstract data types, classes and methods
Encapsulation, inheritance, shadowing
Computational models: random walk simulation
Presenting simulation results, Pylab, plotting
Biased random walks, distributions
Monte Carlo simulations, estimating pi
Validating simulation results, curve fitting, linear regression
Normal, uniform, and exponential distributions misuse of statistics
Stock market simulation
Course overview what do computer scientists do?
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