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Introduction to Computer Science and Programming (Fall 2008 MIT)

(25 Lectures Available)


S#LectureCourseInstituteDiscipline
1 1 Introduction and Goals; Data Types, Operators, and Variables Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
2 2 Branching, Conditionals, and Iteration Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
3 3 Common Code Patterns Iterative Programs Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
4 4 Abstraction through Functions; Introduction to Recursion Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
5 5 Floating Point Numbers, Successive Refinement, Finding Roots Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
6 6 Bisection Methods, Newton/Raphson, Introduction to Lists Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
7 7 Lists and Mutability, Dictionaries, Introduction to Efficiency Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
8 8 Complexity Log, Linear, Quadratic, Exponential Algorithms Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
9 9 Binary Search, Bubble and Selection Sorts Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
10 10 Divide and Conquer Methods, Merge Sort, Exceptions Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
11 11 Testing and Debugging Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
12 12 Debugging, Knapsack Problem, Introduction to Dynamic Programming Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
13 13 Dynamic Programming Overlapping Subproblems, Optimal Substructure Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
14 14 Introduction to Object-oriented Programming Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
15 15 Abstract Data Types, Classes and Methods Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
16 16 Encapsulation, Inheritance, Shadowing Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
17 17 Computational Models Random Walk Simulation Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
18 18 Presenting Simulation Results, Pylab, Plotting Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
19 19 Biased Random Walks, Distributions Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
20 20 Monte Carlo Simulations, Estimating pi Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
21 21 Validating Simulation Results, Curve Fitting, Linear Regression Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
22 22 Normal, Uniform, and Exponential Distributions Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
23 23 Stock Market Simulation Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
24 24 Course Overview; What Do Computer Scientists Do? Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences
25 6 Bisection Methods, Newton/Raphson, Introduction to List Introduction to Computer Science and Programming (Fall 2008 MIT) MIT Applied Sciences

of 1 25 Lectures Available.