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CS110 Introduction to Computer Science and Programming 2008 MIT (24 VIDEOS)

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MIT

6.00  (Fall 2008)
Instructors:
Prof. Eric Grimson
Prof. John Guttag

Level
Undergraduate



Course Home (Click Here)

Many of the problem sets focus on specific topics, such as virus population dynamics, word games, protein sequences, or simulating the movement of a Roomba. (Roomba photograph courtesy of Stephanie Booth on Flickr; virus image courtesy of the CDC; Boggle photograph courtesy of Angelina on Flickr; protein image courtesy of the Lawrence Berkeley National Laboratory.)

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.

Course Resources



Course Lecture Videos

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

Technical Requirements

Special software is required to use some of the files in this course: .py.

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Copyright 2007, by the Contributing Authors. Cite/attribute Resource. administrator. (2009, September 12). CS110 Introduction to Computer Science and Programming 2008 MIT (24 VIDEOS). Retrieved July 31, 2010, from Free University Courses OCW Courses OpenCourseWare Freeversity Foundation Web site: http://www.freeversity.org/science-and-mathematics/computer-science/cs110-introduction-to-computer-science-and. This work is licensed under a Creative Commons License Creative Commons License