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MIT 6.00 Introduction to Computer Science and Programming, Fall 2008

by: MIT OpenCourseWare

Publication date: 2009

Usage: Attribution-Noncommercial-Share Alike 3.0

Topics: Python, programming, computer science, computation, problem solving, recursion, binary search, classes, inheritance, libraries, algorithms, optimization problems, modules, simulation, big O notation, control flow, exceptions, building computational models, software engineering

Language: English

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.

Addeddate: 2009-08-20 12:07:40

Color: color

Identifier: MIT6.00F08

Sound: sound

Year: 2009

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ITEM TILE

MPEG4

118.2M

Lecture 01: Goals of the course; what is computation; introduction to data types, operators, and variables

112.4M

Lecture 02: Operators and operands; statements; branching, conditionals, and iteration

113.5M

Lecture 03: Common code patterns: iterative programs

113.7M

Lecture 04: Decomposition and abstraction through functions; introduction to recursion

97.7M

Lecture 05: Floating point numbers, successive refinement, finding roots

110.2M

Lecture 06: Bisection methods, Newton/Raphson, introduction to lists

102.4M

Lecture 07: Lists and mutability, dictionaries, pseudocode, introduction to efficiency

110.7M

Lecture 08: Complexity; log, linear, quadratic, exponential algorithms

104.8M

Lecture 09: Binary search, bubble and selection sorts

103.0M

Lecture 10: Divide and conquer methods, merge sort, exceptions

108.0M

Lecture 11: Testing and debugging

109.4M

Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming

107.0M

Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure

111.3M

Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming

110.9M

Lecture 15: Abstract data types, classes and methods

111.0M

Lecture 16: Encapsulation, inheritance, shadowing

108.4M

Lecture 17: Computational models: random walk simulation

116.0M

Lecture 18: Presenting simulation results, Pylab, plotting

109.7M

Lecture 19: Biased random walks, distributions

105.2M

Lecture 20: Monte Carlo simulations, estimating pi

118.1M

Lecture 21: Validating simulation results, curve fitting, linear regression

111.7M

Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics

112.5M

Lecture 23: Stock market simulation

94.4M

Lecture 24: Course overview; what do computer scientists do?

227.5M

Lecture 01: Goals of the course; what is computation; introduction to data types, operators, and variables

216.1M

Lecture 02: Operators and operands; statements; branching, conditionals, and iteration

217.4M

Lecture 03: Common code patterns: iterative programs

219.3M

Lecture 04: Decomposition and abstraction through functions; introduction to recursion

188.5M

Lecture 05: Floating point numbers, successive refinement, finding roots

212.8M

Lecture 06: Bisection methods, Newton/Raphson, introduction to lists

197.6M

Lecture 07: Lists and mutability, dictionaries, pseudocode, introduction to efficiency

213.7M

Lecture 08: Complexity; log, linear, quadratic, exponential algorithms

202.6M

Lecture 09: Binary search, bubble and selection sorts

196.8M

Lecture 10: Divide and conquer methods, merge sort, exceptions

207.8M

Lecture 11: Testing and debugging

211.2M

Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming

207.0M

Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure

215.0M

Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming

214.3M

Lecture 15: Abstract data types, classes and methods

214.8M

Lecture 16: Encapsulation, inheritance, shadowing

209.9M

Lecture 17: Computational models: random walk simulation

224.7M

Lecture 18: Presenting simulation results, Pylab, plotting

212.3M

Lecture 19: Biased random walks, distributions

203.9M

Lecture 20: Monte Carlo simulations, estimating pi

228.6M

Lecture 21: Validating simulation results, curve fitting, linear regression

216.6M

Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics

217.8M

Lecture 23: Stock market simulation

182.6M

Lecture 24: Course overview; what do computer scientists do?

SUBRIP

86.8K

Lecture 03: Common code patterns: iterative programs

74.0K

Lecture 10: Divide and conquer methods, merge sort, exceptions

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