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Introduction to Computer Science and Programming | MIT Video Course

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Ikut menyaksikan kuliah di MIT, mengapa tidak?🙂 Yang lebih penting sebetulnya adalah kesempatan tambahan pilihan untuk belajar dengan lebih nyaman. Hampir bisa dikatakan, kapan saja & di mana saja. Bisa diulang-ulang sampai paham apa yang dibicarakan pengajar dan bisa belajar dengan bebas merdeka.

Course Description

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 Pythonprogramming language.

Lectures

  1. Introduction and Goals of the Course Lecture

     

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    Lecture 1 – Introduction and Goals of the Course

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

  2. Operators and operands Lecture

     

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    Lecture 2 – Operators and operands

    Operators and operands; statements; branching, conditionals, and iteration

  3. Common code patterns Lecture

     

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    Lecture 3 – Common code patterns

    Common code patterns: iterative programs

  4. Decomposition and abstraction through functions Lecture

     

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    Lecture 4 – Decomposition and abstraction through functions

    Decomposition and abstraction through functions; introduction to recursion

  5. Floating point numbers Lecture

     

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    Lecture 5 – Floating point numbers

    Floating point numbers, successive refinement, finding roots

  6. Bisection methods Lecture

     

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    Lecture 6 – Bisection methods

    Bisection methods, Newton/Raphson, introduction to lists

  7. Lists and mutability Lecture

     

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    Lecture 7 – Lists and mutability

    Lists and mutability, dictionaries, pseudocode, introduction to efficiency

  8. Complexity Lecture

     

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    Lecture 8 – Complexity

    Complexity; log, linear, quadratic, exponential algorithms

  9. Binary search Lecture

     

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    Lecture 9 – Binary search

    Binary search, bubble and selection sorts

  10. Divide and conquer methods Lecture

     

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    Lecture 10 – Divide and conquer methods

    Divide and conquer methods, merge sort, exceptions

  11. Testing and debugging Lecture

     

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    Lecture 11 – Testing and debugging

    Testing and debugging

  12. Knapsack problem Lecture

     

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    Lecture 12 – Knapsack problem

    More about debugging, knapsack problem, introduction to dynamic programming

  13. Dynamic programming Lecture

     

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    Lecture 13 – Dynamic programming

    Dynamic programming: overlapping subproblems, optimal substructure

  14. Introduction to object-oriented programming Lecture

     

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    Lecture 14 – Introduction to object-oriented programming

    Analysis of knapsack problem, introduction to object-oriented programming

  15. Abstract data types Lecture

     

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    Lecture 15 – Abstract data types

    Abstract data types, classes and methods

  16. Encapsulation Lecture

     

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    Lecture 16 – Encapsulation

    Encapsulation, inheritance, shadowing

  17. Computational models Lecture

     

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    Lecture 17 – Computational models

    Computational models: random walk simulation

  18. Presenting simulation results Lecture

     

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    Lecture 18 – Presenting simulation results

    Presenting simulation results, Pylab, plotting

  19. Biased random walks Lecture

     

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    Lecture 19 – Biased random walks

    Biased random walks, distributions

  20. Monte Carlo simulations Lecture

     

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    Lecture 20 – Monte Carlo simulations

    Monte Carlo simulations, estimating pi

  21. Validating simulation results Lecture

     

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    Lecture 21 – Validating simulation results

    Validating simulation results, curve fitting, linear regression

  22. Normal, uniform, and exponential distributions Lecture

     

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    Lecture 22 – Normal, uniform, and exponential distributions

    Normal, uniform, and exponential distributions; misuse of statistics

  23. Stock market simulation Lecture

     

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    Lecture 23 – Stock market simulation

    Stock market simulation

  24. Course overview: What do computer scientists do? Lecture

     

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    Lecture 24 – Course overview: What do computer scientists do?

    Course overview; what do computer scientists do?

 

 

Written by sunupradana

September 8, 2011 at 8:28 am

Posted in Komputer, Video

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