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,...
Apr 17, 2010
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51794 Views
Lecture 2: Operators and operands; statements; branching, conditionals, and iter...
Apr 17, 2010
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18446 Views
Lecture 3: Common code patterns: iterative programs
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11333 Views
Lecture 4: Decomposition and abstraction through functions; introduction to recu...
Apr 17, 2010
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9631 Views
Lecture 5: Floating point numbers, successive refinement, finding roots
Apr 17, 2010
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7474 Views
Lecture 6: Bisection methods, Newton/Raphson, introduction to lists
Apr 17, 2010
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7400 Views
Lecture 7: Lists and mutability, dictionaries, pseudocode, introduction to effic...
Apr 17, 2010
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6789 Views
Lecture 8: Complexity; log, linear, quadratic, exponential algorithms
Apr 17, 2010
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Lecture 9: Binary search, bubble and selection sorts
Apr 17, 2010
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5746 Views
Lecture 10: Divide and conquer methods, merge sort, exceptions
Apr 17, 2010
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6406 Views
Lecture 11: Testing and debugging
Apr 17, 2010
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5947 Views
Lecture 12: More about debugging, knapsack problem, introduction to dynamic prog...
Apr 17, 2010
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6700 Views
Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure
Apr 17, 2010
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6945 Views
Lecture 14: Analysis of knapsack problem, introduction to object-oriented progra...
Apr 17, 2010
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7186 Views
Lecture 15: Abstract data types, classes and methods
Apr 17, 2010
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4975 Views
Lecture 16: Encapsulation, inheritance, shadowing
Apr 17, 2010
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4495 Views
Lecture 17: Computational models: random walk simulation
Apr 17, 2010
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5802 Views
Lecture 18: Presenting simulation results, Pylab, plotting
Apr 17, 2010
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4242 Views
Lecture 19: Biased random walks, distributions
Apr 17, 2010
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4027 Views
Lecture 20: Monte Carlo simulations, estimating
Apr 17, 2010
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8113 Views
Lecture 21: Validating simulation results, curve fitting, linear regression
Apr 17, 2010
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5694 Views
Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics...
Apr 17, 2010
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4620 Views
Lecture 23: Stock market simulation
Apr 17, 2010
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13252 Views
Lecture 24: Course overview; what do computer scientists do?
Apr 17, 2010
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4728 Views