MIT 6.00 Introduction to Computer Science and Programming - Fall 2008

MIT 6.00 Introduction to Computer Science and Programming - Fall 2008

24 Lectures · Sep 3, 2008

About

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.

Course Homepage: 16.00 Introduction to Computer Science and Programming

Course features at MIT OpenCourseWare page: *Exams *Assignments

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Uploaded videos:

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Lecture 1: Goals of the course; what is computation; introduction to data types,...

Eric Grimson

Apr 17, 2010

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51794 Views

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50:49

Lecture 2: Operators and operands; statements; branching, conditionals, and iter...

Eric Grimson

Apr 17, 2010

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18446 Views

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50:59

Lecture 3: Common code patterns: iterative programs

Eric Grimson

Apr 17, 2010

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11333 Views

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51:26

Lecture 4: Decomposition and abstraction through functions; introduction to recu...

Eric Grimson

Apr 17, 2010

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9631 Views

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Lecture 5: Floating point numbers, successive refinement, finding roots

John Guttag

Apr 17, 2010

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7474 Views

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Lecture 6: Bisection methods, Newton/Raphson, introduction to lists

John Guttag

Apr 17, 2010

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7400 Views

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Lecture 7: Lists and mutability, dictionaries, pseudocode, introduction to effic...

Eric Grimson

Apr 17, 2010

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6789 Views

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50:02

Lecture 8: Complexity; log, linear, quadratic, exponential algorithms

Eric Grimson

Apr 17, 2010

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7852 Views

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Lecture 9: Binary search, bubble and selection sorts

Eric Grimson

Apr 17, 2010

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5746 Views

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

Eric Grimson

Apr 17, 2010

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6406 Views

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Lecture 11: Testing and debugging

John Guttag

Apr 17, 2010

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5947 Views

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

John Guttag

Apr 17, 2010

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6700 Views

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Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure

John Guttag

Apr 17, 2010

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6945 Views

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50:33

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

John Guttag

Apr 17, 2010

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Lecture 15: Abstract data types, classes and methods

Eric Grimson

Apr 17, 2010

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Lecture 16: Encapsulation, inheritance, shadowing

Eric Grimson

Apr 17, 2010

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Lecture 17: Computational models: random walk simulation

John Guttag

Apr 17, 2010

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5802 Views

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Lecture 18: Presenting simulation results, Pylab, plotting

John Guttag

Apr 17, 2010

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Lecture 19: Biased random walks, distributions

John Guttag

Apr 17, 2010

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Lecture 20: Monte Carlo simulations, estimating

John Guttag

Apr 17, 2010

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

John Guttag

Apr 17, 2010

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

John Guttag

Apr 17, 2010

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Lecture 23: Stock market simulation

John Guttag

Apr 17, 2010

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13252 Views

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

John Guttag

Apr 17, 2010

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4728 Views

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