MIT Course , Fall 2008 , Prof. Eric Grimson

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MIT Course , Fall 2008 , Prof. Eric Grimson

Introduction-Operators and operands; statements; branching, conditionals, and iteration-Common code patterns: iterative programs - composition 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 sub problems, 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?

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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- 1.Introduction
- 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, NewtonRaphson, 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?

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