Artificial Intelligence and Machine Learning BSAIML
Degree Awarded: BS in AI & Machine Learning (BSAIML)
Minimum Required Credits: 120.0
Co-op Option: Three Co-op, Two Co-op, One Co-op, No Co-op
Classification of Instructional Programs (CIP) code: 11.0701
Standard Occupational Classification (SOC) code: 15-0000
About the Program
The Bachelor of Science in Artificial Intelligence & Machine Learning (BSAIML) provides a strong foundation in these areas, combining conceptual and theoretical knowledge with large scale deployments and practical applications. The program is designed for maximum flexibility, allowing students to tailor their study of AI and machine learning along specific focus areas (e.g., theory, data analytics, hardware, and/or practical applications). The hands-on curriculum combined with co-op provides real-world experience that culminates in a team capstone project involving in-depth study and application of computing and informatics.
Graduates of the BS AIML program are in high demand in a vast array of industries where knowledge of AI and machine learning is critical for success. Through coursework and possibly double majors, students can also blend their study with a wide variety of other fields, including computing, physical or social sciences, engineering, and arts and humanities.
Additional Information
For more information on this program, please contact the School of Computer and Information Sciences
Degree Requirements
| University Requirements | ||
| EXP 1001 | Introduction to Experiential Learning | 3.0 |
| WRIT 1100 | Composition and Rhetoric I | 3.0 |
| or WRIT 1110 | English Composition I | |
| WRIT 1200 | Composition and Rhetoric II | 3.0 |
| or WRIT 1210 | English Composition II | |
| Introductory Core Competencies 1 | ||
| Select one course from each of the three (3) categories of Introductory Core Competency Courses: | 6.0-9.0 | |
Introductory: Inquire and Analyze | ||
Introductory: Collaborate and Integrate | ||
Introductory: Apply and Engage - satisfied by CIS 1010 in the College Requirements below. | ||
| Free Electives | 15.0 | |
| College Requirements | ||
| The College of Engineering and Computing requires a one-semester first-year design course and a two-semester senior capstone project sequence. Analogous courses across the college may be used as substitutes for the specific courses below; for the senior capstone sequence, college approval is required for any substitutions. | ||
| CIS 1010 | Computer and Information Sciences Design | 3.0 |
| CIS 4998 | Senior Project I | 3.0 |
| CIS 4999 | Senior Project II | 3.0 |
| Program Requirements | ||
| Core Courses | ||
| CS 1030 | Computer Science I | 3.0 |
| CS 1031 | Computer Science II | 3.0 |
| CS 1510 | Introduction to Artificial Intelligence | 3.0 |
| CS 2110 | Data Structures | 3.0 |
| CS 3510 | Principles of Artificial Intelligence | 3.0 |
| CS 3520 | Fundamental Machine Learning | 3.0 |
| CS 3550 | Responsible AI | 3.0 |
| CS 4550 | Modern AI Application Development | 3.0 |
| Electives | ||
| Select five (5) of the following courses, including at least two (2) CS (Computer Science) courses: | 15.0 | |
| Reinforcement Learning | ||
| Game AI Development | ||
| Neurosymbolic Collective Intelligence | ||
| Embodied AI | ||
| Foundations of Deep Learning | ||
| Computer Vision | ||
| Computational Network Neuroscience | ||
| Applied Machine Learning Engineering | ||
| Control Systems | ||
| Digital Signal Processing | ||
| Medical Robotics I | ||
| Pattern Recognition | ||
| Computing and Control | ||
| Applied Robotics Lab | ||
| Decision Making for Robotics | ||
| Bioinformatics | ||
| Statistical Analysis of Genomics | ||
| Optimal Control | ||
| Medical Robotics II | ||
| Cell and Tissue Image Analysis | ||
| Principles of Optimization and Inference | ||
| Applied Machine Learning | ||
| Cloud Computing and Scalable Processing | ||
| Applied Deep Learning | ||
| Deep Learning Architectures | ||
| Social Media Data Analysis | ||
| User Modeling and Recommender Systems | ||
| Modeling Natural Language | ||
| Intelligent Search and Language Models | ||
| Mathematics Requirements | ||
| MATH 1201 | Calculus I | 4.0 |
| MATH 1202 | Calculus II | 4.0 |
| MATH 2401 | Linear Algebra I | 3.0 |
| MATH 2402 | Discrete Mathematics | 3.0 |
| MATH 2801 | Probability and Statistics I | 3.0 |
| Science Requirements | ||
| Select a minimum of six (6) credits from the following options: | 6.0 | |
| General Biology I and General Biology Laboratory I | ||
| General Biology II and General Biology Laboratory II | ||
| General Chemistry I | ||
| General Chemistry II | ||
| Physics I | ||
| Physics II | ||
| Introduction to Earth and Environmental Science | ||
| Free Electives | 19.0 | |
| Optional Co-op Experience | ||
| Co-op is an option for this degree for full-time on-campus students. Co-op cycles may vary. Students choosing this option will be required to complete COOP 1001 as preparation for their co-op experience. COOP 1001 registration is determined by the co-op cycle assigned. | 0.0 | |
| Total Credits | 120.0-123.0 | |
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A complete list of eligible Introductory Core Competency courses can be found here.
Program Learning Outcomes
- Analyze a problem and identify and define the use of artificial intelligence and/or machine learning (AI/ML) as appropriate to its solution
- Interpret and communicate the output of statistical and algorithmic methods
- Function effectively on a team to design and implement a computer-based AI/ML system
- Apply mathematical foundations, algorithmic principles, and computational knowledge in the modeling and design of AI/ML systems in a way that demonstrates comprehension of the tradeoffs involved in design choices
- Design, implement, and evaluate a computer-based AI/ML system, process, component, or program to meet desired needs
- Apply software engineering principles in the construction of computer-based AI/ML systems of varying complexity
- Describe and apply principles of responsible AI