Introduces the practice and research of human-centered computing, including the evolution of human-computer interaction to its forms today and the techniques of user-centered design. The course will survey topics that include social computing; tangible computing; mobility; and more. It will cover computing in society at large with respect to domains such as health, education, assistive technology, emergency response, and environment. Requisites: Restricted to students with 27-180 credits (Sophomores, Juniors or Seniors) only.
Supports students in developing professional skills and practices in human-computer interaction, design of interactive systems, computer supported cooperative work, computer supported collaborative learning, educational technology, tools that support creativity, user-developed knowledge collections, and gaming. May be repeated up to 3 total credit hours. Same as ATLS 3112.
Surveys artificial intelligence techniques of search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language processing. Introduces artificial intelligence programming. Requisites: Requires prerequisite courses of CSCI 2270 and CSCI 2824 or MATH 2001 or ECEN 2703 or APPM 3170 and one of the following: APPM 3570, 4570, 4520, MATH 3510, 4510, CVEN 3227, ECEN 3810 or MCEN 4120 (all minimum grade C-).
Introduces students to fundamental concepts in autonomous, mobile robotics: mechanisms, locomotion, kinematics, control, perception and planning. The course consists of lectures and lab sessions that are geared toward developing a complex robot controller in a realistic, physics-based multi-robot simulator. Same as ECEN 3303. Requisites: Requires prerequisite courses of CSCI 2270 and CSCI 2824 or ECEN 2703 or APPM 3170 or MATH 2001(all minimum grade C-).
Introduces cognitive science, drawing from psychology, philosophy, artificial intelligence, neuroscience, and linguistics. Studies the linguistic relativity hypothesis, consciousness, categorization, linguistic rules, the mind-body problem, nature versus nurture, conceptual structure and metaphor, logic/problem solving and judgment. Emphasizes the nature, implications, and limitations of the computational model of mind. Department enforced prereqs., two of the following: PSYC 2145, LING 2000, CSCI 1300, and PHIL 2440. Same as LING 3005, PHIL 3310, and PSYC 3005.
Exposes students to current research topics in the field of robotics and provides hands-on experience in solving a grand challenge program. Same as CSCI 5302. Requisites: Requires prerequisite course of CSCI 3302 (minimum grade C-).
Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, clustering, and mining specific data types such as time-series, social networks, multimedia, and Web data. Same as CSCI 5502. Requisites: Requires prerequisite course of CSCI 2270 (minimum grade C-).
Exposes students to current research topics in the field of robotics and provides hands-on experience in solving a grand challenge program. Recommended prereq., CSCI 3302 or instructor consent required. Same as CSCI 4302. Requisites: Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.
Examines modern techniques for analyzing and modeling the structure and dynamics of complex networks. Focuses on statistical algorithms and methods, and emphasizes model interpretability and understanding the processes that generate real data. Applications are drawn from computational biology and computational social science. No biological or social science training is required. Recommended prereqs., CSCI 3104 and APPM 3570. Requisites: Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.
Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, clustering, and mining specific data types such as time-series, social networks, multimedia, and Web data. Same as CSCI 4502. Requisites: Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended. Requisites: Requires prerequisite courses of CSCI 2400 and CSCI 3104 (all minimum grade C). Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.
Explores algorithms that can extract information about the world from images or sequences of images. Topics covered include: imaging models and camera calibration, early vision (filters, edges, texture, stereo, optical flow), mid-level vision (segmentation, tracking), vision-based control, and object recognition. Recommended prereq., probability, multivariate calculus, and linear algebra. Requisites: Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.
Introduces a set of modeling techniques that have become a mainstay of modern artificial intelligence, cognitive science and machine learning research. These models provide essential tools for interpreting the statistical structure of large data sets and for explaining how intelligent agents analyze the vast amount of experience that accumulates through interactions with an unfamiliar environment. Recommended prerequisite: undergraduate course in probability and statistics. Requisites: Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.
Explores the field of natural language processing as it is concerned with the theoretical and practical issues that arise in getting computers to perform useful and interesting tasks with natural language. Covers the problems of understanding complex language phenomena and building practical programs. Same as LING 5832. Requisites: Restricted to Computer Science (CSEN) graduate students or Computer Science Concurrent Degree majors only.
Introduction to automatic speech recognition and understanding, conversational agents, dialogue systems, and speech synthesis/text-to-speech. Topics include the noisy channel model, Hidden Markov Models, A* and Viterbi decoding, language modeling (N-grams, entropy), concatenative synthesis, text normalization, dialogue and conversation modeling. Recommended prereqs., CSCI 5582 or 5832, or LING 5200, or instructor consent required. Requisites: Restricted to graduate students only.
Interdisciplinary introduction to cognitive science, examining ideas from cognitive psychology, philosophy, education, and linguistics via computational modeling and psychological experimentation. Includes philosophy of mind; learning; categorization; vision and mental imagery; consciousness; problem solving; decision making, and game-theory; language processing; connectionism. Department enforced prereqs., graduate standing or at least one course at the 3000-level or higher in Computer Science, Linguistics, Philosophy or Psychology. No background in Computer Science will be presumed. Same as EDUC 6504, LING 6200, PHIL 6310, and PSYC 6200. Requisites: Restricted to graduate students only.
Covers advanced theoretical and practical topics in machine learning and latest developments in the field. Students conduct original research, either applied or theoretical, and present their results. Recommended prereq., CSCI 5622 or instructor consent required. Requisites: Restricted to graduate students only.
Topics vary from year to year. Possible topics include human and machine vision, signal and speech processing, artificial life, mathematical foundations of connectionism, and computational learning theory. Recommended prereq., CSCI 5622 or instructor consent required. Requisites: Restricted to graduate students only.
Independent, interdisciplinary research project in cognitive science for graduate students pursuing a joint Ph.D in an approved core discipline and cognitive science. Projects integrate at least two areas within the cognitive sciences: psycology, computer science, linguistics, education, philosophy. Students should obtain commitments from two mentors for their project. Recommended prereqs., CSCI 7762 or EDUC 6505 or LING 7762 or PSYC 7765. Same as LING 7415, PSYC 7415, PHIL 7415, and EDUC 6506. Requisites: Requires a prerequisite course of CSCI 6402 or EDUC 6504 or LING 6200 or PHIL 6310 or PSYC 6200 (minimum grade B). Restricted to graduate students only.
Independent, interdisciplinary research project in cognitive science for advanced graduate students pursuing a joint Ph.D in an approved core discipline and cognitive science. Research projects integrate at least two areas within the cognitive sciences: psychology, computer science, linguistics, education, philosophy. Students need commitments from two mentors for their project. Same as PSYC 7425, LING 7425, PHIL 7425, and EDUC 6516. Requisites: Requires a prerequisite course of LING 7415 or PSYC 7415 or CSCI 7412 or EDUC 6506 (minimum grade B). Restricted to graduate students only.
Reading of interdisciplinary innovative theories and methodologies of cognitive science. Students participate in the ICS Distinguished Speakers series that hosts internationally recognized cognitive scientists who share and discuss their current research. Session discussions include analysis of leading edge and controversial new approaches in cognitive science. Same as LING 7775, PSYC 7775, EDUC 7775, SLHS 7775, and PHIL 7810. Requisites: Restricted to graduate students only.
