Artificial Intelligence, BA
Degree: Bachelor of Arts (BA)
Major: Artificial Intelligence
Program Overview
The Bachelor of Arts degree program in artificial intelligence is a combination of a liberal arts program and an artificial intelligence major. Although it is less technical than the Bachelor of Science degree program in artificial intelligence, it is a professional program in the sense that graduates of this program can be employed as artificial intelligence or computing professionals. The Bachelor of Arts degree program in artificial intelligence is designed according to the latest ACM/AAAI curriculum guidelines for the artificial intelligence area and will give students a strong background in the fundamentals of artificial intelligence and machine learning while also providing students with flexibility to pursue a wide range of academic interests. This program is particularly suitable for students who want to combine expertise in artificial intelligence with expertise in another discipline. For example, students can major in another discipline in addition to artificial intelligence and routinely complete all of the requirements for the double major in a four year period, or students can major in artificial intelligence and take all of the pre-med courses in a four year period. In addition to an excellent technical education, all students in the department have the opportunity to develop professional, leadership and creativity skills.
Mission
The mission of the Bachelor of Arts degree program in artificial intelligence is to graduate students who have fundamental technical knowledge of their profession and the requisite technical breadth and communications skills to become leaders in creating the new techniques and technologies which will advance the field of artificial intelligence and its application to other disciplines.
Program Educational Objectives
Graduates from the Bachelor of Arts degree program in artificial intelligence will be prepared to:
- Analyze real-world problems and create solutions based on the fundamentals of artificial intelligence, mathematics, and computing.
- Work effectively, professionally, collaboratively, and ethically.
- Assume leadership roles in industry, academia, public service, and entrepreneurship.
- Successfully progress in advanced degree programs in artificial intelligence, computing, and related fields.
Learning Outcomes
- Be able to analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
- Be able to design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Be able to communicate effectively in a variety of professional contexts.
- Be able to recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles.
- Be able to function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Be able to apply the theory, techniques, and tools of artificial intelligence, to understand the assumptions implicit in the tools, to make informed judgements about the societal benefits and risks of the applications they are building, and to employ the resulting knowledge to satisfy stakeholders' needs.
Undergraduate Policies
For undergraduate policies and procedures, please review the Undergraduate Academics section of the General Bulletin.
Combined Bachelor's/Master's Programs
Undergraduate students may participate in accelerated programs toward graduate or professional degrees. For more information and details of the policies and procedures related to accelerated studies, please visit the Undergraduate Academics section of the General Bulletin.
Program Requirements
Students seeking to complete this major and degree program must meet the general requirements for bachelor's degrees and the Unified General Education Requirements. Students completing this program as a secondary major while completing another undergraduate degree program do not need to satisfy the school-specific requirements associated with this major.
Artificial Intelligence BA majors complete Required Courses, a Societal Impact Course, Technical Electives, and an Application Elective.
Required Courses
| Code | Title | Credit Hours |
|---|---|---|
| MATH 121 | Calculus for Science and Engineering I | 4 |
| MATH 122 | Calculus for Science and Engineering II | 4 |
| or MATH 124 | Calculus II | |
| MATH 307 | Linear Algebra | 3 |
| STAT 301 | Introduction to Probability for Statistics | 3 |
| or MATH 380 | Introduction to Probability | |
| Choose one of the following: | 3-4 | |
| Programming in Python | ||
| Programming in Java and The Python Programming Language | ||
| CSDS 233 | Introduction to Data Structures | 4 |
| CSDS 302 | Discrete Mathematics | 3 |
| CSDS 310 | Algorithms | 3 |
| CSDS 330 | Introduction to Artificial Intelligence | 3 |
| CSDS 340 | Introduction to Machine Learning | 3 |
| CSDS 395A | Senior Project in Artificial Intelligence | 4 |
| Total Credit Hours | 37-38 | |
Societal Impact Course
| Code | Title | Credit Hours |
|---|---|---|
| Choose one of the following: | 3 | |
| Current Issues in Artificial Intelligence, For Better or Worse | ||
| Responsible AI Engineering | ||
| Algorithmic Fairness | ||
| Ethics of Artificial Intelligence and Emerging Technology | ||
| Responsible AI: Cultivating a Just and Sustainable Socio-technical Future through Data Citizenship | ||
Technical Electives
| Code | Title | Credit Hours |
|---|---|---|
| Choose three of the following: | 9 | |
| Numerical Algorithms for Machine Learning | ||
| Machine Learning on Graphs | ||
| Responsible AI Engineering | ||
| Programming for AI/ML | ||
| Causality and Machine Learning | ||
| Computational Perception | ||
| Computer Vision | ||
| Designing High Performant Systems for AI | ||
| Machine Learning | ||
| Large Language Models | ||
| Probabilistic Models in AI | ||
| Sequential Decision Making | ||
| Foundations of Statistical Natural Language Processing | ||
Application Elective
| Code | Title | Credit Hours |
|---|---|---|
| Choose one 3-4 credit course, or two 1.5 credit courses, from the following: | 3-4 | |
| Data Science | ||
| Structured and Unstructured Data | ||
| Introduction to Data Science Systems | ||
| Introduction to Data Analysis | ||
| Data Mining for Big Data | ||
| High Performance Data and Computing | ||
| Image Processing | ||
| Biomedical Image Processing and Analysis | ||
| AI in Medical Imaging | ||
| Digital Image Processing | ||
| AI in Medicine: Knowledge Representation and Deep Learning | ||
| Mathematics and Statistics | ||
| Convexity and Optimization | ||
| Statistical Computing | ||
| Introduction to Numerical Analysis I | ||
| Mathematics of Data Mining and Pattern Recognition | ||
| Robotics | ||
| Mobile Robotics | ||
| Algorithmic Robotics | ||
| Introduction to Modern Robotics | ||
| Security | ||
| Computer Security | ||
| Data Privacy | ||
| Applied Artificial Intelligence | ||
| Artificial Intelligence for All Disciplines | ||
| Statistical and Machine Learning for Inference, Prediction and Reasoning | ||
| Economics of Artificial Intelligence and Digital Platforms | ||
| Computational Methods for Economic Modeling | ||
| Machine Learning for Predictive Analytics in Economics | ||
| Artificial Intelligence Applications in Healthcare Management | ||
| Artificial Intelligence Fundamentals for Supply Chain Management | ||
| Case Studies in Artificial Intelligence - Supply Chain Applications | ||
| Artificial Intelligence for Biomedical Research | ||
Sample Plan of Study
| First Year | ||
|---|---|---|
| Fall | Credit Hours | |
| MATH 121 | Calculus for Science and Engineering I | 4 |
| CSDS 134 | Programming in Python | 3 |
| Academic Inquiry Seminar, Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Open Elective | 3 | |
| Credit Hours | 16 | |
| Spring | ||
| MATH 122 or MATH 124 | Calculus for Science and Engineering II or Calculus II | 4 |
| CSDS 233 | Introduction to Data Structures | 4 |
| Academic Inquiry Seminar, Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Credit Hours | 14 | |
| Second Year | ||
| Fall | ||
| CSDS 302 | Discrete Mathematics | 3 |
| STAT 301 or MATH 380 | Introduction to Probability for Statistics or Introduction to Probability | 3 |
| Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Open Elective | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| MATH 307 | Linear Algebra | 3 |
| CSDS 330 | Introduction to Artificial Intelligence | 3 |
| Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Open Elective | 3 | |
| Credit Hours | 15 | |
| Third Year | ||
| Fall | ||
| CSDS 340 | Introduction to Machine Learning | 3 |
| Societal Impact Elective or Techical Elective | 3 | |
| Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Open Elective | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| CSDS 310 | Algorithms | 3 |
| Societal Impact Elective or Technical Elective | 3 | |
| Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Open Elective | 3 | |
| Credit Hours | 15 | |
| Fourth Year | ||
| Fall | ||
| Application Elective | 3 | |
| Technical Elective | 3 | |
| Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Open Elective | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| CSDS 395A | Senior Project in Artificial Intelligence | 4 |
| Technical Elective | 3 | |
| Breadth, or Elective course a | 3 | |
| Open Elective | 3 | |
| Open Elective | 2 | |
| Credit Hours | 15 | |
| Total Credit Hours | 120 | |