The curriculum provides a strong foundation in mathematics (calculus, linear algebra, statistics), computer science (programming, algorithms, data structures), and the core domains of AI itself: machine learning, deep learning, natural language processing (NLP), computer vision, and robotics. A key differentiator from general computer science is its deep dive into neural networks, data-driven modeling, and the ethical implications of creating intelligent systems.
The program integrates liberal arts foundations with advanced AI and data science training. It prepares graduates to design, implement, and ethically manage AI-driven systems across industries such as business, healthcare, cybersecurity, robotics, and more
Math, programming, and liberal arts foundations.
AI courses map to TensorFlow, AWS AI, Azure AI, PyTorch certifications
Electives allow specialization in vision, NLP, robotics, healthcare, or ethics
Capstone ensures real-world AI deployment skills
Supports WHU’s mission to produce innovative, ethical AI leaders
Our comprehensive curriculum is structured into different blocks. Each block combines theoretical foundations and practical applications:
Essential liberal arts competencies to support critical thinking, communication, ethics, and quantitative reasoning
| Code | Course title | Credits | Type |
|---|---|---|---|
| ENG 101 | Writing and Composition | 4 | Core |
| ENG 102 | Professional Writing | 4 | Core |
| MATH 120 | Calculus I | 4 | Core |
| STAT 220 | Probability & Statistics for Data Science | 4 | Core |
| HUM 210 | Critical Thinking | 4 | Core |
| PHIL 230 | Ethics of Artificial Intelligence | 4 | Core |
| POLS 210 | AI Policy, Law & Society | 4 | Core |
| ECON 102 | Microeconomics | 4 | Core |
| --- | Natural Sciences or Arts | 4 | Core |
| Credits required: 32 - 36 | |||
| Code | Course title | Credits | Type |
|---|---|---|---|
| AI 201 | Foundations of Computing & Python Programming | 4 | Core |
| AI 210 | Data Structures & Algorithms | 4 | Core |
| AI 220 | Machine Learning Principles | 4 | Core |
| AI 230 | Neural Networks & Deep Learning | 4 | Core |
| AI 240 | Data Science & Big Data Analytics | 4 | Core |
| AI 250 | Natural Language Processing | 4 | Core |
| AI 260 | AI Ethics, Fairness, and Accountability | 4 | Core |
| Credits required: 28 | |||
Students select 5 courses based on interest and career path
| Code | Course title | Credits | Type |
|---|---|---|---|
| AI 301 | Computer Vision & Image Recognition | --- | Elective |
| AI 302 | Reinforcement Learning & Robotics | --- | Elective |
| AI 303 | AI in Cybersecurity | --- | Elective |
| AI 304 | Cloud AI & Scalable Systems | --- | Elective |
| AI 305 | Human-AI Interaction & UX Design | --- | Elective |
| AI 306 | Explainable AI (XAI) & Trustworthy Systems | --- | Elective |
| AI 307 | AI for Healthcare & Bioinformatics | --- | Elective |
| Credits required: 20 | |||
Senior Capstone Project — choose one
| Code | Course title | Credits | Type |
|---|---|---|---|
| AI 400 | Applied AI Internship (industry project) | 4 | Core |
| AI 400 | Directed Research in AI/ML applications | 4 | Core |
| Credits required: 4 | |||
| Component | Credits |
|---|---|
| Foundation & General education | 32 - 36 |
| Core AI courses | 28 |
| Advanced AI electives | 20 |
| Capstone experience | 4 |
| Total credits: 128 | |
As part of the program, students have the opportunity to earn industry-recognized certifications:
A professional credential from Google that validates your foundational skills in building and training neural network models using TensorFlow and Keras. It proves practical competency in building ML models for computer vision and NLP.
Validates your ability to design, implement, deploy, and maintain machine learning solutions on Amazon Web Services (AWS). It covers the entire ML workflow using AWS's AI/ML cloud services like SageMaker.
Validates your skills in using Microsoft Azure's cognitive services, machine learning, and knowledge mining tools to build, manage, and deploy AI solutions. Focuses on implementing solutions for vision, language, speech, and decision-making.
hese are typically course-based specializations (not a single cert) that provide deep, hands-on experience with modern NLP tools and transformer models. You learn to use libraries like Hugging Face transformers and APIs from OpenAI to build and fine-tune models for tasks like translation, summarization, and chatbots.
Focuses on the critical non-technical side of AI. This area covers how to design fair, unbiased, transparent, and accountable AI systems. It addresses algorithmic bias, data privacy, model explainability (XAI), and the societal impact of AI, preparing you to build trustworthy technology.
Graduates of this program have pursued various rewarding career paths:
Focuses on building, deploying, and maintaining production-ready AI systems and infrastructure. They work closely with Data Scientists to scale prototypes into reliable applications that can serve millions of users
Analyzes and interprets complex data to extract insights and inform business decisions. They use statistical analysis, machine learning, and data visualization to solve problems and often create predictive models
A specialist role focused primarily on researching, designing, and implementing machine learning algorithms and models. They are experts in the theory and application of ML
Designs, builds, and programs robots and robotic systems. They combine AI, machine learning, and computer vision to create machines that can perceive and interact with the physical world autonomously
To be considered for admission, applicants must meet the following requirements: