Courses

You must successfully completely a total of 33 credits at the 400, 500, or 800 level, while maintaining a GPA of 3.0 or better in all course work including:

at least 21 credits of required core courses at the 500 or 800 level, with at least 6 credits at the 500 level
at least 9 credits of electives
3 credits of A-I 894, an integrative research topics course, which includes a written paper

Capstone Course

You will apply your knowledge of the theories, methods, processes, and tools of AI, learned throughout your program, in a culminating and summative experience. The choice of project topic and exact form will be mutually determined by you and your instructor. A written paper based on the applied project is required and must contain project description, analysis, and interpretation of findings.

Course List - Master of Professional Studies in Artificial Intelligence

Core Courses (21 credits)
Title	Abbreviation	Description	Credits
Natural Language Processing	A-I 574	This course covers basic as well as advanced concepts to gain a detailed understanding of Natural Language Processing tasks such as language modeling, text to speech generation, natural language understanding, and natural language generation. Students can learn the necessary skills to design a range of applications, including sentiment analysis, translating between languages, and answering questions. The practical implementation of these applications with deep neural networks is also discussed.	3 credits
Foundations of Artificial Intelligence	A-I 801	This course will teach the foundations of AI and give students a practical understanding of the field. This course gives an overview of the core concepts of AI, including the intelligent agents, knowledge and reasoning, reinforcement learning, planning and acting, belief networks, computational learning, Markov decision process, and more.	3 credits
Machine Vision	A-I 879	This course focuses on the design of computer-based, machine vision systems using appropriate algorithms and best practices. Students will learn image representation and structuring; feature extraction and segmentation; and information extraction, filtering, and analysis.	3 credits
Deep Learning	DAAN 570	This course will cover the foundations on neural networks and deep learning networks. It covers the core concepts of deep neural networks, including the convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation. Prerequisite: STAT 500	3 credits
Analytics Programming in Python	DAAN 862	This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.	3 credits
Applied Statistics	STAT 500	Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, Chi-square tests, diagnostics. Prerequisite: one undergraduate course in statistics	3 credits
Ethics and Values in Science and Technology	STS 589	Study interrelationships of twentieth century technological change and human values with emphasis on social and ethical aspects of technological progress.	3 credits

Electives (choose 9 credits)
Title	Abbreviation	Description	Credits
Reinforcement Learning	DAAN 572	This course will cover the main theory and approaches of reinforcement learning, along with deep learning and common software libraries and packages.	3 credits
Data Collection and Cleaning	DAAN 822	Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large datasets. Prerequisite: STAT 500 and INSC 521	3 credits
Network and Predictive Analytics for Soci-Technical Systems	DAAN 846	The objective of this course is to provide a foundation in the principles of network and predictive analytics along with hands-on experience with statistical analysis software for studying the interrelatedness of cyber-social and cyber-technical aspects of our society as a whole. Prerequisite: CSE 453 or IST 815	3 credits
Data Visualization	DAAN 871	This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.	3 credits
Data-Driven Decision Making	DAAN 881	The theory and application of several quantitative decision-making tools will be studied. The usefulness of these tools will be illustrated using projects and case studies throughout the course. Emphasis will be placed on the application of the tools and techniques and the results they generate. Prerequisite: STAT 500	3 credits
Foundations of Predictive Analytics	IE 575	Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system. Prerequisite: IE 323, STAT 500 or equivalent	3 credits
Data Mining	SWENG 545	Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software. Prerequisite: INSCI 521 or approval of instructor or department	3 credits

Culminating Experience (3 credits)
Title	Abbreviation	Description	Credits
Research Topic	A-I 894	The choice of project topic and exact form will be mutually determined by you and your instructor. A written paper based on the applied project is required and must contain project description, analysis, and interpretation of findings.	3 credits

Course Availability

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