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Course Criteria
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4.00 Credits
This introductory course gives an overview of machine learning. This is a wide ranging field including topics such as: classification, linear regression, Principal Component Analysis (PCA), neural networks, bagging and boosting, support vector machines, hidden Markov models, Bayesian networks, Q-learning, reinforcement learning.
Prerequisite:
CMSC 310 and (MATH 117, MATH 217, or MATH 375)
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3.00 Credits
Topics include finite automata, regular languages, regular expressions, and regular grammars; pushdown automata and context-free languages; Turing machines; Church-Turing Thesis; the Halting Problem; undecidability; classes of languages, including the Chomsky hierarchy and the classes P, NP, and NP-Complete. Proof techniques for showing language (non)membership in a class. This course is not available for graduate credit.
Prerequisite:
CMSC 310 (Grade of C or Higher)
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4.00 Credits
Studies protocol suites, emphasizing the TCP/IP 4-layer model. Topics included are network addresses, sub netting, client/server network programming via the sockets API, network utilities, architecture of packets, routing, fragmentation, connection and termination, connection-less applications, data flow, and an examination of necessary protocols at the link layer, particularly Ethernet. Other topics may include FDDI, wireless, ATM, congestion control, and network security. This class is available for graduate credit.
Prerequisite:
CMPE 220 (Grade of C or Higher) or SWEN 200 (Grade of C or Higher)
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4.00 Credits
Overview of artificial intelligence. Emphasis on basic tools of AI, search and knowledge representation, and their application to a variety of AI problems. Search methods include depth-first, breadth-first, and AI algorithms; knowledge representation schemes include propositional and predicate logics, semantic nets and frames, and scripts. Planning using a STRIPS-like planner will also be addressed. Areas that may be addressed include natural language processing, computer vision, robotics, expert systems, and machine learning. This class is available for graduate credit.
Prerequisite:
CMPE 211 (Grade of C or Higher) or SWEN 200 (Grade of C or Higher)
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3.00 Credits
Detailed examination of theory and practical issues underlying the design, development, and use of a DBMS. Topics include characteristics of a well-designed database; high-level representation of an application using ER modeling; functional dependency theory, normalization, and their application toward a well-designed database; abstract query languages; query languages; concurrency; integrity; security. Advanced topics may be included (e.g., distributed databases; object-oriented databases). Theory to practice is applied in a number of projects involving the design, creation, and use of a database. This class is not available for graduate credit.
Prerequisite:
CMPE 211 (Grade of C or Higher) or SWEN 200 (Grade of C or Higher)
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3.00 Credits
Opportunity to offer courses in areas of departmental major interest not covered by the regular courses. This class is available for graduate credit.
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3.00 Credits
Opportunity to offer courses in areas of departmental major interest not covered by the regular courses. This class is available for graduate credit.
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3.00 Credits
Opportunity to offer courses in areas of departmental major interest not covered by the regular courses. This class is available for graduate credit.
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4.00 Credits
Presents topics that will vary according to need. Topics such as languages are appropriate. This class is available for graduate credit.
Prerequisite:
CMSC 111, MATH 211, and MATH 318
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4.00 Credits
Opportunity to offer courses in areas of departmental major interest not covered by the regular courses. This class is available for graduate credit.
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