Session Overview
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This lecture continues the discussion of curve fitting, emphasizing the interplay among theory, experimentation, and computation and addressing the problem of over-fitting. It then moves on to introduce the notion of an optimization problem, and illustrates it using the 0/1 knapsack problem. Image courtesy of kob42kob on Flickr. |
Session Activities
Lecture Videos
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Lecture 18: Optimization Problems and Algorithms (00:49:42)
Lecture 18: Optimization Problems and Algorithms
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About this Video
Topics covered: Modeling, optimization, greedy algorithms, 0-1 knapsack problem.
Resources
Check Yourself
What does an optimization problem consist of?
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An optimization problem requires that an objective function be optimized, either by maximizing or minimizing the function. There may also be a set of constraints that must be accounted for.
What is problem reduction?
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Taking a problem with an unknown solution and reducing it to a problem or problems with known solutions.
Problem Sets
Problem Set 8: Simulating The Spread of Disease and Virus Population (Due)
In this problem set, using Python and pylab you will design and implement a stochastic simulation of patient and virus population dynamics, and reach conclusions about treatment regimens based on the simulation results.
- Instructions (PDF)
- Code Files (ZIP) (This ZIP file contains: 1 .py file and 1 .pyc file.)
- Solutions (ZIP) (This ZIP file contains: 1 .py file and 1 .pdf file.)
Problem Set 9 (Assigned)
Problem set 9 is assigned in this session. The instructions and solutions can be found on the session page where it is due, Lecture 20 More Clustering.