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Course Criteria
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3.00 Credits
Lec 3. Prereq: CSCE 156 or permission. High-level processing for image understanding and high-level vision. Data structures, algorithms, and modeling. Low-level representation, basic pattern-recognition and image-analysis techniques, segmentation, color, texture and motion analysis, and representation of 2-D and 3-D shape. Applications for content-based image retrieval, digital libraries, and interpretation of satellite imagery.
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3.00 Credits
Lec 3. Prereq: CSCE 310; STAT/MATH 380 or STAT 880. CSCE 474/874 requires the completion of a project involving the application of data mining techniques to real-world problems. Data mining and knowledge discovery methods and their application to real-world problems. Algorithmic and systems issues. Statistical foundations, association discovery, classification, prediction, clustering, spatial data mining and advanced techniques.
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3.00 Credits
Lec 3. Prereq: CSCE 310. Distributed problem solving and planning, search algorithms for agents, distributed rational decision making, learning multiagent systems, computational organization theory, formal methods in Distributed Artificial Intelligence, multiagent negotiations, emergent behaviors (such as ants and worms), and Robocup technologies and real-time coalition formation.
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3.00 Credits
Lec 3. Prereq: CSCE 310. Introduction to basic principles, techniques, and tools now being used in the area of machine intelligence. Languages for AI programming introduced with emphasis on LISP. Lecture topics include problem solving, search, game playing, knowledge representation, expert systems, and applications.
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3.00 Credits
Lec 3. Prereq: CSCE 310, MATH 314/814 or equivalent. Introductory course on cryptography and computer security. Topics: classical cryptography (substitution, Vigenere, Hill and permutation ciphers, and the one-time pad); Block ciphers and stream ciphers; The Data Encryption Standard; Public-key cryptography, including RSA and El-Gamal systems; Signature schemes, including the Digital Signature Standard; Key exchange, key management and identification protocols.
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3.00 Credits
Lec 3. Prereq: CSCE 310. STAT 380 recommended. Introduction to the fundamentals and current trends in machine learning. Possible applications for game playing, text categorization, speech recognition, automatic system control, date mining, computational biology, and robotics. Theoretical and empirical analyses of decision trees, artificial neural networks, Bayesian classifiers, genetic algorithms, instance-based classifiers and reinforcement learning.
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3.00 Credits
Lec 3. Introduction to the concepts, design and application of connection- based computing begins by simulating neural networks, focusing on competing alternative network architectures, including sparse distributed memories, Hopfield networks, and the multilayered feed-forward systems. Construction and improvement of algorithms used for training of neural networks addressed to reduce training time and improve generalization. Algorithms for training and synthesizing effective networks implemented in high level language programs running on conventional computers. Emphasis on methods for synthesizing and simplifying network architectures for improved generalization. Application areas include: pattern recognition, computer vision, robotics medical diagnosis, weather and economic forecasting.
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1.00 Credits
Lec 1. Prereq: CSCE 361. CSCE 486 must be taken exactly one semester before CSCE 487. Preparation for the senior design project. Professional practice through familiarity with current tools, resources, and technologies. Professional standards, practices and ethics, and the oral and written report styles used specifically in the field of computer science.
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3.00 Credits
Lec 3. Prereq: Senior standing; CSCE 361 and 486. CSCE 487 uses the team approach to undertake a substantial, broadly-defined project requiring aggregation of the technical and analytical skills learned in other CSCE courses.
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2.00 Credits
Lec 2. Prereq: JGEN 200; ELEC 362 and 476, or parallel; CSCE 430/830 or parallel; formal admission to College of Engineering. CSCE 488 is a preparation course for the computer engineering Senior Design Project (CSCE 489). CSCE 488 and 489 are a sequence of courses that are to be taken in consecutive terms. Professional practice through familiarity and practice with current tools, resources, and technologies; professional standards, practices, and ethics; and oral and written report styles used in the computer engineering field.
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