Demonstrations
From 6.034 Wiki
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* Learning: nearest neighbors, support vector machines, lattice learning, boosting | * Learning: nearest neighbors, support vector machines, lattice learning, boosting | ||
* Neural nets: autocoding, logistic regression | * Neural nets: autocoding, logistic regression | ||
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'''The demonstration software is unavailable temporarily pending renewal of a certificate. | '''The demonstration software is unavailable temporarily pending renewal of a certificate. | ||
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If you don't have the Java 8 Runtime Environment installed, | If you don't have the Java 8 Runtime Environment installed, | ||
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Then, you | Then, you | ||
can run the [https://courses.csail.mit.edu/6.034f/demonstrations/demonstrations.jnlp latest version]. | can run the [https://courses.csail.mit.edu/6.034f/demonstrations/demonstrations.jnlp latest version]. | ||
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Revision as of 15:47, 13 October 2018
Much of the material in 6.034 is reinforced by on-line artificial-intelligence demonstrations develop by us or otherwise available on the web. Those demonstrations developed by us are provided via the easy-to-use Java Web Start mechanism, which comes with the Java Runtime Environment, the so-called JRE.
The demonstrations illustrate the following ideas:
- Blocks world manipulation (after Winograd)
- Search: depth-first, breadth-first, hill-climbing, beam, branch and bound, A*
- Games: mini-max, alpha-beta
- Genetic algorithms: crossover, mutation, fitness
- Constraint satisfaction: drawing analysis (after Waltz, using Huffman labels)
- Domain reduction: map coloring, resource allocation
- Biological mimetics: genetic algorithms, self-organizing maps, cross-modal clustering
- Learning: nearest neighbors, support vector machines, lattice learning, boosting
- Neural nets: autocoding, logistic regression
If you don't have the Java 8 Runtime Environment installed, you should install it first.
Next, because Java security becomes steadily greater, you will need to do some preparation.
Then, you can run the latest version.
