artificial intelligence
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
notes of
abstract
This subject was studied online on MIT platforms and the course number is 6.034.
a course from MIT
detailed description
This subject has the following study approach, respected on the published notes:
1. Scope;
2. Reasoning: goal trees on problem solving, goal trees on rule-based expert systems;
3. Search: depth-first, breadth-first, british museum, hill climbing, beam, optimal, branch and bound complexities, a*, games, minimax, alpha-beta;
4. Constraints: interpreting line drawings, search, domain reduction, visual object recognition;
5. Learning: introduction to learning, nearest neighbors, identification trees, disorder, neural nets, back propagation, genetic algorithms, sparse spaces, phonology, near misses, felicity conditions, support vector machines, boosting;
6. Representations: classes, trajectories, transitions;
7. Architectures: GPS, SOAR, Subsumption, Society Of Mind;
8. Probabilistic Inferencing;
9. Modal Merging and Cross-Modal Coupling.
On the library of this page resides 1 book which can be opened. In order to achieve it, you must create an Apontamentos account, on the bottom of this page. The books are available, then, on the Introdução à Inteligência Artificial (iia) subject.
books available
discussion
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