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Master of Professional Studies in
Artificial Intelligence

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. 

Core Courses (21 credits)

  • 3
    credits

    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

    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

    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

    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

    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

    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

      Study interrelationships of twentieth century technological change and human values with emphasis on social and ethical aspects of technological progress.

    • or:
      3
      credits

      The Ethics of Artificial Intelligence is the young branch of applied ethics that seeks to study the far-reaching and diverse ethical issues that arise with the widespread and rapid integration of AI technologies into various aspects of our lives.

Electives (select 9 credits)

  • 3
    credits

    This course will cover the main theory and approaches of reinforcement learning, along with deep learning and common software libraries and packages.

  • 3
    credits

    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

    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

    This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

  • 3
    credits

    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

    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

    Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.

    • Prerequisite

      INSC 521, or approval of instructor or department

Culminating Experience (3 credits)

  • 3
    credits

    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 Availability

If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.

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