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:
Lecture 1: Goals of the course; what is computation; introduction to data types,...
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51804 Views
Lecture 2: Operators and operands; statements; branching, conditionals, and iter...
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Lecture 3: Common code patterns: iterative programs
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Lecture 4: Decomposition and abstraction through functions; introduction to recu...
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Lecture 5: Floating point numbers, successive refinement, finding roots
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Lecture 6: Bisection methods, Newton/Raphson, introduction to lists
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Lecture 7: Lists and mutability, dictionaries, pseudocode, introduction to effic...
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Lecture 8: Complexity; log, linear, quadratic, exponential algorithms
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Lecture 9: Binary search, bubble and selection sorts
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Lecture 10: Divide and conquer methods, merge sort, exceptions
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Lecture 11: Testing and debugging
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Lecture 12: More about debugging, knapsack problem, introduction to dynamic prog...
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Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure
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Lecture 14: Analysis of knapsack problem, introduction to object-oriented progra...
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Lecture 15: Abstract data types, classes and methods
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Lecture 16: Encapsulation, inheritance, shadowing
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Lecture 17: Computational models: random walk simulation
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Lecture 18: Presenting simulation results, Pylab, plotting
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Lecture 19: Biased random walks, distributions
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Lecture 20: Monte Carlo simulations, estimating
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Lecture 21: Validating simulation results, curve fitting, linear regression
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Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics...
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Lecture 23: Stock market simulation
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Lecture 24: Course overview; what do computer scientists do?
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