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Friday, 27 September 2013

6.00SC - Introduction to Computer Science and Programming - MIT VIDEO LECTURES

6.00SC - Introduction to Computer Science and Programming - MIT VIDEO LECTURES
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 Python programming language.
Download Link (MIT Lecture Notes):
> DOWNLOAD "6.00SC - Introduction to Computer Science and Programming - MIT LECTURE NOTES" <

Lecture Video Files MPEG4 Ogg Video
Lecture 01: Goals of the course; what is computation; introduction to data types, operators, and variable 116.4 MB
212.6 MB
Lecture 02: Operators and operands; statements; branching, conditionals, and iteration 110.7 MB
195.1 MB
Lecture 03: Common code patterns: iterative programs 110.6 MB
200.5 MB
Lecture 04: Decomposition and abstraction through functions; introduction to recursion 112.5 MB
185.1 MB
Lecture 05: Floating point numbers, successive refinement, finding roots 96.3 MB
171.4 MB
Lecture 06: Bisection methods, Newton/Raphson, introduction to lists 109.5 MB
164.2 MB
Lecture 07: Lists and mutability, dictionaries, pseudocode, introduction to efficiency 101.0 MB
173.5 MB
Lecture 08: Complexity; log, linear, quadratic, exponential algorithms 109.0 MB
183.3 MB
Lecture 09: Binary search, bubble and selection sorts 103.3 MB
185.6 MB
Lecture 10: Divide and conquer methods, merge sort, exceptions 100.7 MB
175.8 MB
Lecture 11: Testing and debugging 106.7 MB
186.8 MB
Lecture 12: More about debugging, knapsack problem, introduction to dynamic programming 108.1 MB
201.8 MB
Lecture 13: Dynamic programming: overlapping subproblems, optimal substructure 106.8 MB
173.9 MB
Lecture 14: Analysis of knapsack problem, introduction to object-oriented programming 109.9 MB
208.9 MB
Lecture 15: Abstract data types, classes and methods 110.3 MB
182.1 MB
Lecture 16: Encapsulation, inheritance, shadowing 110.1 MB
175.8 MB
Lecture 17: Computational models: random walk simulation 107.6 MB
185.5 MB
Lecture 18: Presenting simulation results, Pylab, plotting 115.6 MB
182.5 MB
Lecture 19: Biased random walks, distributions 108.8 MB
177.9 MB
Lecture 20: Monte Carlo simulations, estimating pi 104.8 MB
176.7 MB
Lecture 21: Validating simulation results, curve fitting, linear regression 116.9 MB
207.5 MB
Lecture 22: Normal, uniform, and exponential distributions; misuse of statistics 110.4 MB
195.3 MB
Lecture 23: Stock market simulation 111.8 MB
192.7 MB
Lecture 24: Course overview; what do computer scientists do? 94.6 MB
129.2 MB

Prof. John Guttag, 6.00SC, Introduction to Computer Science and Programming
(Massachusetts Institute of Technology: MIT OpenCouseWare), (Accessed September 27, 2013). 
Our website abides by the Creative Commons BY-NC-SA as set by MIT.


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