MIT - Introduction to Computer Science and Programming

Tutorial & e-learning. May 1, 2011 by sputnik.


MIT - Introduction to Computer Science and Programming|2.54 GB
Genre: Elearning

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

LECTURES

Lecture 1 - Introduction and Goals of the Course
Goals of the course; what is computation; introduction to data types, operators, and variables

Lecture 2 - Operators and operands
Operators and operands; statements; branching, conditionals, and iteration

Lecture 3 - Common code patterns
Common code patterns: iterative programs

Lecture 4 - Decomposition and abstraction through functions
Decomposition and abstraction through functions; introduction to recursion

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

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

Lecture 7 - Lists and mutability
Lists and mutability, dictionaries, pseudocode, introduction to efficiency

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

Lecture 9 - Binary search
Binary search, bubble and selection sorts

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

Lecture 11 - Testing and debugging
Testing and debugging

Lecture 12 - Knapsack problem
More about debugging, knapsack problem, introduction to dynamic programming

Lecture 13 - Dynamic programming
Dynamic programming: overlapping subproblems, optimal substructure

Lecture 14 - Introduction to object-oriented programming
Analysis of knapsack problem, introduction to object-oriented programming

Lecture 15 - Abstract data types
Abstract data types, classes and methods

Lecture 16 - Encapsulation
Encapsulation, inheritance, shadowing

Lecture 17 - Computational models
Computational models: random walk simulation

Lecture 18 - Presenting simulation results
Presenting simulation results, Pylab, plotting

Lecture 19 - Biased random walks
Biased random walks, distributions

Lecture 20 - Monte Carlo simulations
Monte Carlo simulations, estimating pi

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

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

Lecture 23 - Stock market simulation
Stock market simulation

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

More info: http://academicearth.org/courses/introduction-to-computer-science-and-programming

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