We have a new AI Concentration! You will be able to declare the concentration when you register for courses.
Learning Objectives and Outcomes
Graduates of the AI concentration will be able to:
- Explain core machine learning algorithms (supervised, unsupervised, reinforcement).
- Implement and tune deep learning models using modern frameworks.
- Apply AI in domains such as NLP, computer vision, and generative AI.
- Assess ethical, societal, and operational implications of AI systems.
- Communicate AI results and capabilities to technical and non-technical audiences.
Curriculum Structure
Total Credits: 30 credits (10 courses)
Core Courses
- DSAN 5000 – Data Science and Analytics
- DSAN 5100 – Probability and Statistics
- DSAN 5200 – Advanced Data Visualization
- DSAN 5300 – Statistical Learning
- DSAN 6000 – Big Data and Cloud Computing
Required AI Courses
- DSAN 6600 – Neural Networks and Deep Learning
- DSAN 6650 – Reinforcement Learning
- DSAN 6500 – Computer Vision or DSAN 5800 – Advanced NLP
- DSAN 6725 – Applied Generative AI for AI Developers
Elective (Select one)
- DSAN 5400 – Computational Linguistics
- DSAN 6100 – Optimization
- DSAN 6400 – Network Analytics
- DSAN 5600 – Applied Time Series
- DSAN 5800 – Advanced NLP (if not taken above)
- DSAN 6550 – Adaptive Measurement with AI
- DSAN 7000 – Advanced Research (Capstone Project)
Sample Course Plans
Example: Two Year Plan
Fall- Year One
- DSAN 5000
- DSAN 5100
- DSAN 5400
Spring-Year One
- DSAN 5200
- DSAN 5300
- DSAN 6600
Fall- Year Two
- DSAN 6000
- DSAN 6650
- DSAN 6500
Spring-Year Two
- DSAN 6725
Example: 1.5 Year Plan
Fall-Year One
- DSAN 5000
- DSAN 5100
- DSAN 6600
Spring-Year One
- DSAN 5200
- DSAN 5300
- DSAN 6725
Summer-Year One
- DSAN 6400
Fall- Year Two
- DSAN 6000
- DSAN 6650
- DSAN 6500 or DSAN 7000
Career Pathways
Graduates will be prepared for roles such as:
- Machine Learning Engineer
- AI Research Scientist
- NLP/Computer Vision Specialist
- AI Solutions Architect
- and any other field requires AI expertise.
Faculty and Resources
Courses will be taught by experienced DSAN faculty with expertise in:
- Deep learning
- NLP and computational linguistics
- Reinforcement learning and optimization
- Industry connections with AWS, Microsoft, etc.
- Students will also have access to:
- High-performance computing resources at DSAN
- Opportunities for interdisciplinary AI research collaborations (e.g., with CSET, McDonough and McCourt Schools)
Commitment to Diversity
The track supports Georgetown’s DEI goals through:
- Flexible course schedules
- Project-based learning
- Accessibility to nontraditional and underrepresented student groups
Implementation & Assessment Plan
- Launch Date: Fall 2025
- Governance: Under DSAN program
- Assessment:
- Track enrollment trends
- Graduate exit surveys
- AI-related placement monitoring
- Industry board review