Artificial Intelligence for Business

The Artificial Intelligence for Business major is designed to address two broad topics: (1) the (more) technical understanding of methods and how they are being applied by firms to solve business problems and (2) the (more) conceptual understanding of how the technology impacts firms and society, including economic, social, and ethical issues that AI deployment introduces. Reflecting this conceptualization, the major has two pillars, F&I:

1. Foundations of AI in Business (or F)
2. Impact and Ethical Implications of AI in Business (or I)

Courses in category “F” cover foundational skills needed to understand, work with, and/or evaluate AI systems. Such courses can cover, but are not limited to, subjects such as data engineering, data science, statistics, machine learning, or neuroscience.

Courses in category “I” cover the business, economic, legal, ethical, psychological, or societal impact of AI.

Requirements for Major:

Students will be required to fulfill a course requirement for each pillar:

“F”– STAT 7230: Applied Machine Learning in Business (1 CU)

“I”– LGST 6420: Big Data, Big Responsibilities: Toward Accountable Artificial Intelligence (0.5 CU)

Thus, 1.5 CU comes from required courses.

Students are then required to complete an equivalent of 4 CU in total, by taking other courses in the two course categories:

• 2 CU’s must be from Category “F” (of which 1 CU is a required class, see above).
• 2 CU’s must be from Category “I” (of which 0.5 CU is a required class, see above).

Of these, 1 CU can come from a list of approved courses outside Wharton. 

Courses outside of Wharton must be approved by the faculty advisors.

Category “F” Approved Courses (2 CU’S REQUIRED)

OIDD 5810: Convex Optimization for Statistics and Data Science
STAT 5810: Convex Optimization for Statistics and Data Science
HCMG 8570: Healthcare Data and Analytics
MKTG 7370: Introduction to Brain Science for Business
MKTG 7680: Contagious: How Things Catch On
STAT 5710: Modern Data Mining
STAT 5850: Foundations of Deep Learning with Applications
OIDD 6620: Enabling Technologies
MKTG 7120: Data and Analysis for Marketing Decisions
FNCE 7370: Data Science for Finance
OIDD 7770: Introduction to Python for Data Science
FNCE 7800: FinTech
HCMG 8530: Management and Strategy in Medical Devices and Technology
MKTG 9560: Empirical Models in Marketing – Part A

Category “I” Approved Courses (2 CU’S REQUIRED)

OIDD 6130: Online Business Models and the Information-Based Firm
MKTG 7310: Technology Strategy
MKTG 7340: Idea Generation and the Systematic Approach for Creativity
MKTG 7790: AI in Our Lives: The Behavioral Science of Autonomous Technology
MGMT 8020: Innovation, Change, and Entrepreneurship

Courses taken on a pass/fail basis cannot be counted toward the major. A maximum of 1 CU of an Independent Study Project (ISP) can count toward the major with advisor approval.