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6.00 Introduction to Computer Science and Programming

6.00 Introduction to Computer Science and Programming (Fall 2008, MIT OCW). Instructors: Professor Eric Grimson and Professor John Guttag. This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language. (from ocw.mit.edu)

Lecture 20 - Monte Carlo Simulations, Estimating pi


Go to the Course Home or watch other lectures:

Lecture 01 - Introduction and Goals; Data Types, Operators, and Variables
Lecture 02 - Branching, Conditionals, and Iteration
Lecture 03 - Common Code Patterns: Iterative Programs
Lecture 04 - Abstraction through Functions; Introduction to Recursion
Lecture 05 - Floating Point Numbers, Successive Refinement, Finding Roots
Lecture 06 - Bisection Methods, Newton/Raphson, Introduction to Lists
Lecture 07 - Lists and Mutability, Dictionaries, Introduction to Efficiency
Lecture 08 - Complexity: Log, Linear, Quadratic, Exponential Algorithms
Lecture 09 - Binary Search, Bubble and Selection Sorts
Lecture 10 - Divide and Conquer Methods, Merge Sort, Exceptions
Lecture 11 - Testing and Debugging
Lecture 12 - Debugging, Knapsack Problem, Introduction to Dynamic Programming
Lecture 13 - Dynamic Programming: Overlapping Subproblems, Optimal Substructure
Lecture 14 - Introduction to Object-oriented Programming
Lecture 15 - Abstract Data Types, Classes and Methods
Lecture 16 - Encapsulation, Inheritance, Shadowing
Lecture 17 - Computational Models: Random Walk Simulation
Lecture 18 - Presenting Simulation Results, Pylab, Plotting
Lecture 19 - Biased Random Walks, Distributions
Lecture 20 - Monte Carlo Simulations, Estimating pi
Lecture 21 - Validating Simulation Results, Curve Fitting, Linear Regression
Lecture 22 - Normal, Uniform, and Exponential Distributions
Lecture 23 - Stock Market Simulation
Lecture 24 - Course Overview; What Do Computer Scientists Do?