Minor in Applications of Artificial Intelligence and Machine Learning
The interdisciplinary minor in Applications of Artificial Intelligence and Machine Learning will equip undergraduate students with skills and knowledge to use AI and ML to solve problems in engineering, humanities, and social sciences. The curriculum is designed to also provide students with the insight to describe and discuss current ethics and policy frameworks related to AI and machine learning.
The minor is open to students pursuing majors in the Ivan Allen College of Liberal Arts, the College of Engineering, the Scheller College of Business, and the College of Sciences. For up-to-date information on which majors are eligible to participate, as well as contact info for the corresponding advisers, please see the website https://sites.gatech.edu/apps-ai-ml-minor/. The minor consists of four tracks. Any student admitted to the minor is eligible to choose either track. No “mix and match” is allowed between the tracks, so students are strongly encouraged to discuss with their academic advisor which track best matches their interest and career goals, and which is most compatible with major requirements.
The Minor in Applications of Artificial Intelligence and Machine Learning has the following Learning Outcomes:
Upon completion of this minor, all students will be able to:
- Describe and discuss current ethics and policy frameworks relating to AI/ML, in the United States and internationally.
Additionally, students in the Engineering track (as well as those taking Engineering elective courses) will be able to:
- Describe methods in probability and statistics and apply them to solve engineering and/or math problems.
- Describe models and algorithms of AI/ML and apply them to solve engineering problems.
Additionally, students in the Ivan Allen College track as well as those taking Ivan Allen elective courses) will be able to:
- Describe methods in probability and statistics and apply them to solve problems in humanities and social sciences.
- Describe models and algorithms of AI/ML and apply them to solve problems in humanities and social sciences.
Additionally, students in the Scheller College track (as well as those taking Scheller elective courses) will be able to:
- Describe methods in probability and statistics and apply them to solve problems in business.
- Describe models and algorithms of AI/ML and apply them to solve problems in business.
Additionally, students in the College of Sciences track (as well as those taking Science elective courses) will be able to:
- Describe methods in probability and statistics and apply them to solve problems in the sciences and mathematics.
- Describe models and algorithms of AI/ML and apply them to solve problems in the sciences and mathematics.
Guidelines for Minor:
- Research credits must be related to AI/ML to count for the minor. Each Unit will designate a specific person to review and approve applicability of a student’s research credits towards the minor. Only 3 credit hours of research may be counted as an elective for this minor.
- IAC and Scheller students choosing the Engineering track and taking BMED 2400 for the minor can fulfill the BMED 2400 pre-requisites with MATH 1712 and CS 1315 or CS 1301.
- Students choosing the Scheller College track must take CS 1301 or an equivalent approved programming language course to satisfy the prerequisite requirement for “AI in Business”.
- At least two courses must be taken outside of the student’s home school. Cross-listed courses cannot count as being “outside the home school” for any of the students who are from the schools that cross-list that course.
- Courses must be taken from two or more schools.
- All courses from the minor must be passed with a grade of C or higher.
- A minimum of 9 credits must be at the 3000-level or above.
- No course counted towards this minor can be used for any other undergraduate minor or certificate.
- Besides the Institute restrictions outlined in the Catalog, this minor does not have any additional restrictions on double-counting courses between the minor and the major.
Engineering Track
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Core Course 1 - Select one from the following: | 3 | |
| Introduction to Bioengineering Statistics | ||
| Statistics and Applications | ||
| Basic Statistical Methods | ||
| Prob/Stats for ECE | ||
| Probability and Statistics with Applications | ||
| Statistics and Numerical Methods in Materials Science and Engineering | ||
| Core Course 2 - Select one of the following: | 3 | |
| Foundations in Machine Learning for Engineers | ||
| Data Analytics for Chemical Engineers | ||
| Fundamentals of Machine Learning (FunML) | ||
| Introduction to Machine Learning for Biomedical Engineers | ||
| Special Topics (Foundations of Scientific Machine Learning) | ||
| Data Analytics in Civil and Environmental Engineering | ||
| Regression and Forecasting | ||
| Methods and Applications of Machine Learning | ||
| Core Course 3 | ||
| PHIL 3101 | AI Ethics and Policy | 3 |
| Elective courses | ||
| Select two of the following: | 6 | |
| Biomed-AI and Health Informatics | ||
| Introduction to Medical Image Processing | ||
| Introduction to Bioinformatics | ||
| Special Topics (Art and Generative AI ) | ||
| AI For Smart Cities | ||
| Special Topics (Complex Systems in CEE and AI Control ) | ||
| Data-Driven Process Systems Engineering | ||
| Special Topics (AI for Experimental Chemical Engineering) | ||
| Introduction to Signal Processing | ||
| AI Foundations | ||
| Optimization for Information Systems | ||
| Digital Image Processing | ||
| Fundamentals of Digital Signal Processing | ||
| Applications of Digital Signal Processing | ||
| Machine Learning for Economics | ||
| Applications of Linguistics | ||
| Language & Computers | ||
| Science, Technology, and International Affairs | ||
| International Affairs and Technology Policy Making | ||
| Race, Gender, and Digital Media | ||
| Special Topics (Responsible AI in Communication ) | ||
| Modeling and Control of Motion Systems | ||
| Robotics | ||
| Special Topics (AI for Design and Manufacturing ) | ||
| Special Topics (Machine Learning Methods for Mechanical Engineering) | ||
| Constraint Programming | ||
| Online Learning & Decision Making | ||
| Special Topics (Foundations of Modern Data Science ) | ||
Research Credit (3 hours max with approval) | ||
| Total Credit Hours | 15 | |
Ivan Allen College Track
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses: | ||
| Core Course 1 - Select one of the following: | 3 | |
| Statistics for Economists | ||
| Statistical Analysis for Public Policy | ||
| Core Course 2 - Select one of the following: | 3 | |
| Special Topics in Economics (Introduction to Data Science for Economics) | ||
| Data Science for Public Policy | ||
| Special Topics (Data Analytics and Security ) | ||
| Core Course 3 | ||
| PHIL 3101 | AI Ethics and Policy | 3 |
| Elective Courses | ||
| Select 2 of the following: | 6 | |
| Machine Learning for Economics | ||
| Applications of Linguistics | ||
| Language & Computers | ||
| Science, Technology, and International Affairs | ||
| International Affairs and Technology Policy Making | ||
| Race, Gender, and Digital Media | ||
| Special Topics (Responsible AI in Communication) | ||
| Special Topics (Art and Generative AI ) | ||
| Games, Computers, and Intelligence | ||
| Sociology of Work, Industry, and Occupations | ||
Research Credit (3 hours max with approval) | ||
| Total Credit Hours | 15 | |
Scheller College Track
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses: | ||
| Core Course 1: | ||
| MGT 2250 | Management Statistics | 3 |
| Core Course 2: | ||
| MGT 4803 | Special Topics in Management (AI in Business) | 3 |
| Core Course 3: | ||
| PHIL 3101 | AI Ethics and Policy | 3 |
| Elective Courses | 6 | |
| Select 2 of the following: | ||
| Machine Learning for Economics | ||
| Applications of Linguistics | ||
| Language & Computers | ||
| Science, Technology, and International Affairs | ||
| Sociology of Work, Industry, and Occupations | ||
| Marketing Research: Analytics | ||
| Applications of Data Analytics in Accounting | ||
| Business Analytics | ||
| Understanding Markets with Data Science | ||
| Managing Process Innovation | ||
| Machine Learning for Business | ||
| Special Topics in Management (AI and ML in Finance) | ||
| Special Topics in Management (NLP and GenAI in Finance) | ||
Research Credit XXXX 4699 (3 hours max with approval) | ||
| Total Credit Hours | 15 | |
College of Sciences Track
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Core Course 1 - Select one from the following: | 3 | |
| Experimental Design and Statistical Methods in Biological Sciences | ||
| Quantitative Techniques in Earth and Atmospheric Sciences | ||
| Environmental Data Analysis | ||
| Introduction to Probability and Statistics | ||
| Probability and Statistics with Applications | ||
| Probability and Statistics for Computing and Machine Learning | ||
| Special Topics (Applied Statistics for Neuroscientists ) | ||
| Special Topics (Data Science for Physicists ) | ||
| Psychological Statistics | ||
| Core Course 2 - Select one of the following: | 3 | |
| Introductory Data Science and Machine Learning for Science | ||
| Mathematical Foundations of Data Science | ||
| Computational Methods for Simulation and Machine Learning | ||
| Special Topics (AI for Cognitive Science ) | ||
| Core Course 3: | ||
| PHIL 3101 | AI Ethics and Policy | 3 |
| Elective Courses | 6 | |
| Select two of the following: | ||
| Proteomics: Technologies & Applications | ||
| Special Topics (Nanosensors in Biomedicine) | ||
| Earth System Modeling | ||
| Special Topics (Machine Learning in Earth and Environmental Sciences) | ||
| Quantum Information and Quantum Computing | ||
| Computational Physics | ||
| Physics of Cognition | ||
| Human Language Processing | ||
| Special Topics in Neuroscience (The Math of the Mind: An Introduction to Computational Cognitive Neuroscience ) | ||
| Neuroethics (The Math of the Mind: An Introduction to Computational Cognitive Neuroscience) | ||
| Psychological Testing | ||
| Neuro AI Models of the Brain and Mind | ||
| Machine Learning for Economics | ||
| Science, Technology, and International Affairs | ||
| Applications of Linguistics | ||
| Language & Computers | ||
| International Affairs and Technology Policy Making | ||
| Race, Gender, and Digital Media | ||
| Special Topics (Responsible AI in Communication ) | ||
| Special Topics (Art and Generative AI) | ||
| Data Science for Public Policy | ||
Research Credit XXXX 4699 (3 hours max with approval) | ||
| Total Credit Hours | 15 | |