Artificial Intelligence, BS
Degree: Bachelor of Science (BS)
Major: Artificial Intelligence
Program Overview
The Bachelor of Science degree program in Artificial Intelligence is designed to give a student a strong background in the fundamentals of artificial intelligence and machine learning. The curriculum is designed according to the latest ACM/AAAI curriculum guidelines for the artificial intelligence knowledge area. A graduate of this program will be able to determine when an artificial intelligence approach is appropriate for a given problem, identify appropriate representations and reasoning mechanisms, implement them, and evaluate them with respect to both performance and their broader societal impact, including potential risks. A graduate will be able to design and implement systems that are state-of-the-art solutions to a variety of problems that arise in artificial intelligence, including problems that are sufficiently complex to require the evaluation of design alternatives and engineering trade-offs. In addition to these program-specific objectives, students can use their technical and open electives to pursue interests in fundamental issues, knowledge representation and reasoning, machine learning, probabilistic representation and reasoning, planning and decision making, natural language processing, robotics, perception, and computer vision. All students in the Case School of Engineering are exposed to societal issues, professionalism, and are provided opportunities to develop leadership skills.
Mission
The mission of the Bachelor of Science degree program in Artificial Intelligence is to graduate students who have fundamental technical knowledge of their profession and the requisite technical breadth and communication skills to become leaders in creating the new techniques and technologies that will advance the field of artificial intelligence and its application to other disciplines.
Program Educational Objectives
Graduates from the Bachelor of Science in Artificial Intelligence program 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.
Co-op and Internship Programs
Opportunities are available for students to alternate studies with work in industry or government as a co-op student, which involves paid full-time employment over seven months (one semester and one summer). Students may work in one or two co-ops, beginning in the third year of study. Co-ops provide students the opportunity to gain valuable hands-on experience in their field by completing a significant engineering project while receiving professional mentoring. During a co-op placement, students do not pay tuition but maintain their full-time student status while earning a salary. Alternatively or additionally, students may obtain employment as summer interns.
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.
The AI BS Major consists of 22 AI and AI-related courses totaling 68 units. In addition to the general degree requirements and a minimum of 123 total units, each AI BS major student must complete the Math and Engineering Requirement, the Science Requirement, and the following major requirements:
- AI Core
- Foundations
- AI Breadth
- Societal Impact
- Secure Computing
- Technical Electives
AI-related courses not listed here may be used to satisfy specific requirements with prior permission from the student's academic advisor.
Math and Engineering Requirement
| 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 223 | Calculus for Science and Engineering III | 3 |
| or MATH 227 | Calculus III | |
| ENGR 399 | Impact of Engineering on Society | 3 |
| Total Credit Hours | 14 | |
Science Requirement
The Science Requirement can be satisfied by completing one of the sequences listed below. Students wishing to combine AI with other science disciplines can alternatively satisfy the Science Requirement either by completing one of the following minor programs: Biology, Chemistry, or Geological Sciences; or by completing one of the following major programs: Biology, Biochemistry, Chemistry, Geological Sciences, or Neuroscience.
| Code | Title | Credit Hours |
|---|---|---|
| Choose one of the following: | 4 | |
| Introductory Physics I | ||
| General Physics I - Mechanics | ||
| Physics and Frontiers I - Mechanics | ||
| Choose one of the following: | 4 | |
| Introductory Physics II | ||
| General Physics II - Electricity and Magnetism | ||
| Physics and Frontiers II - Electricity and Magnetism | ||
| Total Credit Hours | 8 | |
AI Core Requirement
| Code | Title | Credit Hours |
|---|---|---|
| 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 | 23-24 | |
Foundations Requirement
Students must demonstrate competence across a range of mathematical disciplines used in different areas of AI. Students wishing to have a more in-depth and rigorous background can consider using Group 2 Technical Electives to complete a two-course sequence in probability and statistical inference (STAT 301 and STAT 302), numerical methods and optimization (MATH 431 and MATH 433), or both.
| Code | Title | Credit Hours |
|---|---|---|
| MATH 307 | Linear Algebra | 3 |
| Choose one of the following: | 3 | |
| Introduction to Probability | ||
| Introduction to Probability for Statistics | ||
| Choose one of the following: | 3 | |
| Numerical Algorithms for Machine Learning | ||
| Convexity and Optimization | ||
| Statistical Computing | ||
| Introduction to Numerical Analysis I | ||
| Total Credit Hours | 9 | |
AI Breadth Requirement
| Code | Title | Credit Hours |
|---|---|---|
| Choose six from the following: | 18 | |
| 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 | ||
| Algorithmic Fairness | ||
| AI in Medical Imaging | ||
| Large Language Models | ||
| Probabilistic Models in AI | ||
| Sequential Decision Making | ||
| Foundations of Statistical Natural Language Processing | ||
Societal Impact Requirement
AI BS students must demonstrate competence in assessing both the risks and benefits of AI technology by completing at least one of the courses listed below.
| 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 | ||
Secure Computing Requirement
Students must demonstrate competence in the principles and practices of secure computing by completing one of the following courses.
Technical Electives
Four technical electives are required. The list of approved technical electives is divided into groups according to how closely a course is related to the core knowledge areas as defined in the ACM/AAAI computer science curriculum guidelines in the AI knowledge area.
Students may use at most two courses from Group 2 to count as technical electives toward the AI BS degree. Courses may not be double counted.
AI-related courses not listed below may be used as technical electives but require prior permission from the department chair or their designee.
Group 1
| Code | Title | Credit Hours |
|---|---|---|
| Any AI Breadth course, in addition to the following: | ||
| Data Science | ||
| CSDS 234 | Structured and Unstructured Data | 3 |
| CSDS 312 | Introduction to Data Science Systems | 3 |
| CSDS 313 | Introduction to Data Analysis | 3 |
| CSDS 335 | Data Mining for Big Data | 3 |
| CSDS 435 | Data Mining | 3 |
| CSDS 438 | High Performance Data and Computing | 3 |
| Image Processing | ||
| CSDS 361 | Biomedical Image Processing and Analysis | 3 |
| CSDS 490 | Digital Image Processing | 3 |
| MATH 444 | Mathematics of Data Mining and Pattern Recognition | 3 |
| PQHS 416 | AI in Medicine: Knowledge Representation and Deep Learning | 3 |
| Robotics | ||
| CSDS 376 | Mobile Robotics | 4 |
| CSDS 499 | Algorithmic Robotics | 3 |
| CSDS 589 | Robotics II | 3 |
| ECSE 373 | Introduction to Modern Robotics | 4 |
| Applied AI | ||
| CSDS 102 | Artificial Intelligence for All Disciplines | 3 |
| DSCI 353 | Statistical and Machine Learning for Inference, Prediction and Reasoning | 3 |
| SYBB 464 | Artificial Intelligence for Biomedical Research | 3 |
Group 2
Students may use at most two courses from Group 2 to count as technical electives toward the AI BS degree. Courses may not be double counted.
| Code | Title | Credit Hours |
|---|---|---|
| Any CSDS course, in addition to the following: | ||
| MATH 330 | Introduction to Scientific Computing | 3 |
| MATH 382 | High Dimensional Probability | 3 |
| MATH 406 | Mathematical Logic and Model Theory | 3 |
| MATH 408 | Introduction to Cryptology | 3 |
| MATH 433 | Numerical Solutions of Nonlinear Systems and Optimization | 3 |
| PHIL 306 | Mathematical Logic and Model Theory | 3 |
| STAT 302 | Introduction to Statistical Inference | 3 |
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 | 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 |
| PHYS 121 | General Physics I - Mechanics | 4 |
| CSDS 233 | Introduction to Data Structures | 4 |
| Academic Inquiry Seminar, Breadth, or Elective course | 3 | |
| Credit Hours | 15 | |
| Second Year | ||
| Fall | ||
| MATH 223 or MATH 227 | Calculus for Science and Engineering III or Calculus III | 3 |
| MATH 307 | Linear Algebra | 3 |
| PHYS 122 | General Physics II - Electricity and Magnetism | 4 |
| Technical Elective | 3 | |
| Breadth, or Elective course | 3 | |
| Credit Hours | 16 | |
| Spring | ||
| MATH 380 or STAT 301 | Introduction to Probability or Introduction to Probability for Statistics | 3 |
| CSDS 302 | Discrete Mathematics | 3 |
| CSDS 330 | Introduction to Artificial Intelligence | 3 |
| Breadth, or Elective course | 3 | |
| Open Elective | 3 | |
| Credit Hours | 15 | |
| Third Year | ||
| Fall | ||
| CSDS 310 | Algorithms | 3 |
| CSDS 340 | Introduction to Machine Learning | 3 |
| AI Foundations Elective | 3 | |
| AI Breadth Elective | 3 | |
| Breadth, or Elective course | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| ENGR 399 | Impact of Engineering on Society | 3 |
| AI Breadth Elective | 3 | |
| Societal Impact or AI Breadth Elective | 3 | |
| AI Secure Computing Elective | 3 | |
| Breadth, or Elective course | 3 | |
| Credit Hours | 15 | |
| Fourth Year | ||
| Fall | ||
| AI Breadth Elective | 3 | |
| Societal Impact or AI Breadth Elective | 3 | |
| Technical Elective | 3 | |
| Technical Elective | 3 | |
| Breadth, or Elective course | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| CSDS 395A | Senior Project in Artificial Intelligence | 4 |
| AI Breadth Elective | 3 | |
| AI Breadth Elective | 3 | |
| Technical Elective | 3 | |
| Breadth, or Elective course | 3 | |
| Credit Hours | 16 | |
| Total Credit Hours | 123 | |