Course Information

Home
Video Lectures
About The Professor

Course

Code:	6.00
Subject:	Computer Science
Topic:	Programming
Views:	79,627

Educator

Name:	Massachusetts Institute of Technology (MIT)
Type:	University

Visit Official Website

Support the MIT OpenCourseWare program

Donate to MIT

Instructor

Name

Eric Grimson

Introduction to Computer Science and Programming (Fall 2008)

Video Lectures

Displaying all 24 video lectures.

Lecture 1 Play Video	Goals, Introduction to the Course Lecture 1: Goals of the course; what is computation; introduction to data types, operators, and variables
Lecture 2 Play Video	Operators, operands, statements, branching, iteration Lecture 2: Operators and operands; statements; branching, conditionals, and iteration
Lecture 3 Play Video	Common code patterns: iterative programs Lecture 3: Common code patterns: iterative programs
Lecture 4 Play Video	Decomposition, abstraction through functions and recursion Lecture 4: Decomposition and abstraction through functions; introduction to recursion
Lecture 5 Play Video	Floating point numbers, finding roots Lecture 5: Floating point numbers, successive refinement, finding roots
Lecture 6 Play Video	Bisection methods, Newton/Raphson, introduction to lists Lecture 6: Bisection methods, Newton/Raphson, introduction to lists
Lecture 7 Play Video	Lists, mutability, dictionaries, pseudocode and efficiency Lecture 7: Lists and mutability, dictionaries, pseudocode, introduction to efficiency
Lecture 8 Play Video	Complexity; log, linear, quadratic, exponential algorithms Lecture 8: Complexity; log, linear, quadratic, exponential algorithms
Lecture 9 Play Video	Binary search, bubble and selection sorts Lecture 9: Binary search, bubble and selection sorts
Lecture 10 Play Video	Divide and conquer methods, merge sort, exceptions Lecture 10: Divide and conquer methods, merge sort, exceptions
Lecture 11 Play Video	Testing and debugging Lecture 11: Testing and debugging
Lecture 12 Play Video	Debugging, knapsack problem, dynamic programming Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming
Lecture 13 Play Video	Dynamic programming: overlapping subproblems, optimal substructure Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure
Lecture 14 Play Video	Knapsack problem and object-oriented programming Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming
Lecture 15 Play Video	Abstract data types, classes and methods Lecture 15: Abstract data types, classes and methods
Lecture 16 Play Video	Encapsulation, inheritance, shadowing Lecture 16: Encapsulation, inheritance, shadowing
Lecture 17 Play Video	Computational models: random walk simulation Lecture 17: Computational models: random walk simulation
Lecture 18 Play Video	Presenting simulation results, Pylab, plotting Lecture 18: Presenting simulation results, Pylab, plotting
Lecture 19 Play Video	Biased random walks, distributions Lecture 19: Biased random walks, distributions
Lecture 20 Play Video	Monte Carlo simulations, estimating pi Lecture 20: Monte Carlo simulations, estimating pi
Lecture 21 Play Video	Simulation results, curve fitting, linear regression Lecture 21: Validating simulation results, curve fitting, linear regression
Lecture 22 Play Video	Normal, uniform, and exponential distributions Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics
Lecture 23 Play Video	Stock market simulation Lecture 23: Stock market simulation
Lecture 24 Play Video	Course overview; what do computer scientists do? Lecture 24: Course overview; what do computer scientists do?