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Master of Science in artifical intelligence

Required Courses

Course Name: GRAD 50200- Interdisciplinary AI Fundamentals: Bridging Knowledge 

Credit Hours: 1 
Course Length: 4 Weeks 

 
Description: This one-credit hour course provides foundational concepts for AI for those in both MSAI majors. It covers technological change leading up to modern AI, how AI systems are designed, the foundations of artificial reasoning, and practical skills for conceptualizing and communicating about AI systems and technology. Course materials are designed to be accessible to learners with diverse backgrounds, serves as a transdisciplinary introduction to AI. Regardless of your area of expertise, you will gain an understanding of critical AI concepts, principles, and their real-world applications. You do not need a programming or math background, though experience in using technology will support your success in this course.   
 
Learning Outcomes: 

1. Describe the foundations of artificial reasoning, knowledge, and trace the historical development of AI. 

2. Explain the basic concepts, terminologies, and applications of different AI algorithms, developing a problem-solving mindset applicable to various domains. 

3. Examine the diverse range of AI applications across varied industries. 

4. Explain the role human factors and social context play in the development of artificial intelligence. 

 
Course Name: SCLA 52200 Artificial Intelligence Policy, Governance, And Ethics 

Credit Hours: 3 
Course Length: 8 weeks 
 
Description: This course provides students with an introduction to the policy, ethical, and legal regulatory dimensions that shape the governance of AI. Students will analyze current and emerging laws, regulations, and governance strategies that impact the AI landscape across multiple jurisdictions and sectors. The course will help students gain an interdisciplinary understanding of the core ethical principles and values that guide AI regulation and policy.    
 
 
Learning Outcomes: 

1. Describe key stakeholders, institutions, and strategies involved in AI policymaking across multiple levels of government (local to global) in multiple fields and explain their roles in governing AI systems. 

2. Evaluate the legal, regulatory, and other governance strategies that have been proposed or adopted by governments, firms, and civil society organizations, such as model cards, liability frameworks, standards, and impact assessments. 

3. Analyze ethical and social issues related to the development and application of AI systems within multiple fields, including power, model bias, transparency, accountability, worker well-being, and privacy, as well as the translation of ethical values into technical aspects of AI governance practices within public and private, and non-profit sector organizations. 

4. Synthesize, analyze, and communicate information about emerging policy issues in AI and the associated ethical and social dimensions, identify stakeholder perspectives, and summarize proposed solutions. 

5. Assess current and evolving trends, debates, and solutions in AI governance and ethics through case studies, contemporary issues, and comparative analysis. 

 
Course Name: SCLA 52100 Societal Impacts of Artificial Intelligence 

Credit Hours: 3 
Course Length: 8 weeks 
 
Description: Students in this course will draw upon knowledge from multiple fields to understand how AI is transforming social, political, and economic practices in institutions and in society. They will utilize interdisciplinary perspectives to evaluate the societal impacts of AI. The course draws on a mix of case and source data combined with recent research in the field of AI to create opportunities for shared communication and learning among those in technical and non-technical fields related to artificial intelligence. 
 
Learning Outcomes: 

1. Describe how AI is transforming social, political, and economic institutions, practices and employment in multiple sectors, including healthcare, education, finance, communications, criminal justice and others. 

2. Explain the nature of risks of AI in society and outline common strategies to manage risks. 

3. Evaluate core theories used to understand AI’s potential societal impact on connectedness, trust, creativity, and conflict. 

4. Assess and communicate AI impacts by applying interdisciplinary tools drawing from social sciences, humanities, and STEM fields. 

5. Identify the human rights implications of AI and understand potential mitigation practices. 

 
Course Name: GRAD 58900 Master of Science in Artificial Intelligence Capstone 

Credit Hours: 3 
Course Length: 16 weeks 
 
Description: The capstone course aims to provide students with an opportunity to integrate their accumulated knowledge, technical, and social skills to identify and solve a realistic or real-world artificial intelligence problem, with an emphasis on the application domain. The capstone course for the MSAI provides students with practical experience applying the collective set of skills developed through the MSAI program to complete a professional project in support of a private, public, or non-profit partner. Students, in teams, will select a target beneficiary, arrange a consultation, develop a problem statement, a project plan that will yield benefits, and a strategy for measuring the outcome of the project compared to a baseline. 
 
Learning Outcomes: 

1. Identify the public, non-profit, or business objectives in a complex problem. 

2. Evaluate and define an applied problem using AI that requires practical analysis and recommendations / and analytic output to a stakeholder. 

3. Design a professional-level applied project that provides meaningful input to a targeted beneficiary. 

4. Evaluate the proposed solution and interpret results concerning the “business” objectives. 

5. Demonstrate an understanding of translational communication skills by communicating technical information to both technical and non-technical audiences. 

AI Management & Policy Major Core Courses 

Course Name: GRAD 50300-AI Essentials: A Non-Technical Introduction 

Credit Hours: 3 
Course Length: 8 weeks 
 
Description: This course provides students on the translator track a foundation in the principles and methodologies of Artificial Intelligence (AI) to empower them to actively engage in interdisciplinary collaborations. With a focus on the essentials of AI, machine learning, and natural language processing, students will gain the knowledge and skills needed to bridge the gap between language and technology. Students will leverage AI tools and techniques to enhance translation efficiency, accuracy, and engagement in the ever-evolving digital landscape. Students will have a foundation in the methodology so they can effectively participate in interdisciplinary collaborations, serving as a valuable resource for industries where language and technology converge. 
 
Learning Outcomes: 

1. Explain the basic concepts and applications of different AI algorithms and how they apply for specific fields for application or research purposes. 

2. Describe the types of data inputs and outputs used in different AI applications. 

3. Define a project and apply basic AI approaches in a programming environment. 

4. Communicate methods and results of an AI project to specialists and non-specialists. 

AI & Machine Learning Core Courses 

Course Name: GRAD 50400-Advanced AI Fundamentals for Technical Professionals 

Credit Hours: 3 
Course Length: 8 weeks 
 
Description: Artificial Intelligence (AI) is the driving force behind the transformation of industries, research, and technology. For students with a highly technical background, this 2-credit hour graduate course offers a deep dive into the fundamental principles, theories, and applications of AI. Specifically, this course will introduce students to the field of data mining and machine learning, which sits at the interface between statistics and computer science. Data mining and machine learning focus on developing algorithms to automatically discover patterns and learn models of large datasets. This course introduces students to the process and main techniques in data mining and machine learning, including exploratory data analysis, predictive modeling, descriptive modeling, and evaluation. 
 
Learning Outcomes: 

1. Identify key elements of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. 

2. Understand how to choose AI/ML algorithms for different analysis tasks. 

3. Analyze data in both an exploratory and targeted manner using AI and ML approaches. 

4. Implement and apply basic AI/ML algorithms for supervised and unsupervised learning. 

5. Accurately evaluate the performance of AI/ML algorithms, as well as formulate and test hypotheses.