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Home » Applied Sciences » Computer Science » Introduction to Computer Science and Programming (Spring 2011) (M-I-T) » Lecture 19: More Optimization and Clustering (M-I-T)

Lecture 19: More Optimization and Clustering (M-I-T)

Course:Introduction to Computer Science and Programming (Spring 2011) (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): John Guttag
Level:Undergraduate

Introduction to Computer Science and Programming (Spring 2011) (M-I-T)

Lecture 10: Hashing and Classes (M-I-T)
Lecture 11: Object Oriented Programming (OOP) and Inheritance (M-I-T)
Lecture 12: Introduction to Simulation and Random Walks (M-I-T)
Lecture 13: Some Basic Probability and Plotting Data (M-I-T)
Lecture 14: Sampling and Monte Carlo Simulation (M-I-T)
Lecture 15: Statistical Thinking (M-I-T)
Lecture 16: Using Randomness to Solve Non-random Problems (M-I-T)
Lecture 17: Curve Fitting (M-I-T)
Lecture 18: Optimization Problems and Algorithms (M-I-T)
Lecture 19: More Optimization and Clustering (M-I-T)
Lecture 1: Introduction to Computer Science and Programming (M-I-T)
Lecture 20: More Clustering (M-I-T)
Lecture 21: Using Graphs to Model Problems, Part 1 (M-I-T)
Lecture 22: Using Graphs to Model Problems, Part 2 (M-I-T)
Lecture 23: Dynamic Programming (M-I-T)
Lecture 24: Avoiding Statistical Fallacies (M-I-T)
Lecture 25: Queuing Network Models (M-I-T)
Lecture 26: What Do Computer Scientists Do? (M-I-T)
Lecture 2: Core Elements of a Program (M-I-T)
Lecture 3: Problem Solving (M-I-T)

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