Artificial Intelligence<\/a> CSCI151 HM Machine Learning<\/a> CSCI158 HM Natural Language Processing<\/a> CSCI159 HM Machine Learning: Theory & Application<\/a> ENGR208 HM
Credit(s): 3
Instructor(s): Boerkoel, Talvitie
Description: This course presents a general introduction to the field of Artificial Intelligence. It examines the question: What does (will) it take for computers to perform human tasks? It presents a broad introduction to topics such as knowledge representation, search, learning and reasoning under uncertainty. For each topic, it examines real-world applications of core techniques to problems which may include game playing, text classification and visual pattern recognition.
Prerequisite(s): CSCI070 HM<\/a> and (MATH056 HM<\/a> or MATH062 HM<\/a> or BIOL154 HM<\/a> or MATH151 CM\/PO\/PZ\/SC)<\/p>\n\n\n\n
Credit(s): 3
Instructor(s): Monta\u00f1ez, Talvite
Description: Machine learning is concerned with the study and development of systems that learn patterns in data. This course introduces the most common problems in the field and the techniques used to tackle these problems, with a focus on supervised and unsupervised learning. Concepts include mathematical foundations and algorithmic approaches.
Prerequisite(s): CSCI070 HM<\/a>, MATH073 HM<\/a>, and (MATH056 HM<\/a> or MATH062 HM<\/a> or BIOL154 HM<\/a> or MATH151 CM\/PO\/PZ\/SC); CSCI151 HM<\/a> recommended<\/p>\n\n\n\n
Credit(s): 3
Instructor(s): Medero, Schofield
Description: An introduction to the fundamental concepts and ideas in natural language processing, sometimes called computational linguistics. The goals of the field range from text translation and understanding to enabling humans to converse with robots. We will study language processing starting from the word level to syntactic structure to the semantic meaning of text. Approaches include structured and statistical methods, as well as exploration of current natural language research. Students will build and modify systems and will use large existing corpora for validating their systems.
Prerequisite(s): CSCI081 HM<\/a> and (MATH056 HM<\/a> or MATH062 HM<\/a> or BIOL154 HM<\/a> or MATH151 CM\/PO\/PZ\/SC)<\/p>\n\n\n\nEngineering<\/h3>\n\n\n\n
Credit(s): 3
Instructor(s): Staff
Offered: Fall
Description: An introduction to modern machine learning methods and their application to signals. Students will learn to design, train, and use modern machine learning models. These may include, but are not limited to dense neural networks, convolutional neural networks, and recurrent neural networks.
Prerequisite(s): ENGR101 HM<\/a> and CSCI060 HM<\/a>, or permission of instructor. <\/p>\n\n\n\nHumanities, Social Sciences, and the Arts<\/h3>\n\n\n\n