Understanding AI Technologies
for Business Problems
MKTG 321 | Stanford Graduate School of Business
Instructor: Yuyan Wang
Open to MBA and MSx students
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Understanding AI Technologies
This course aims to equip students with practical AI and ML knowledge for solving real-world business problems. The main focus will be demystifying these technical concepts to help future product managers, investors, and entrepreneurs better communicate with technical people on the team. We will start by introducing the vocabulary used in AI and ML and then dive into some technical discussions on how to build these AI technologies to solve specific business problems. The topics covered range from recommender systems, multi-sided platforms, human-centered AI, and generative AI, with technical discussions on multi-objective optimization, machine learning and reinforcement learning, and large language models (LLMs).
There will also be guest lecturers from across the industry to provide additional insights and real-world examples. This course is recommended for MBA2 and MSx students who have already completed Data and Decisions, and MBA1s with a strong statistical background.
Course Instructor
Course Instructor
Yuyan Wang
Assistant Professor of Marketing
Kevin J. O’Donohue Family Faculty Scholar for 2024-2025
Course Feedback
Course Feedback
"In one word, 'WOW.' This course was of such a high caliber and Prof. Wang is such an amazing professor who has the gift of being able to discuss complex things in simple, understandable language while also going into depths if needed or in response to questions. "
— anonymous student
"This is one of the best classes I have taken within the GSB the perfect blend of technical concepts and business application! Professor Wang has deep industry experience and shared with us the reality of using AI in her line of work. She also really helped demystified AI! "
— anonymous student
"Yuyan is amazing! She not only has great domain expert knowledge, but also has real world experience in implementing AI in business applications. She cares a lot about students' learning and also welcome any questions. It's just great to have such an amazing and caring professor. "
— anonymous student
Course Evaluation Statistics:
How much did you learn from this course?
Overall mean = 4.7; median = 5How would you rate the course content overall?
Overall mean = 4.85; median = 5How would you describe the quality of the instruction in this course?
Overall mean = 4.9; median = 5
Course Syllabus
Course Syllabus
Last taught: Winter Quarter 2025
01/07/2025 - 03/14/2025
Download the latest syllabus:
COURSE SCHEDULE
COURSE SCHEDULE
Introduction & The Vocabulary of AI
Learnings + Q&A | Guest Speaker
Deep Learning & Reinforcement Learning
Learnings + Q&A | Guest Speaker
AI for Personalization: Recommender Systems
Learnings + Q&A | Guest Speaker
AI for Multi-Sided Platforms
Learnings + Q&A | Guest Speaker
GenAI & LLMs (part 1)
GenAI & LLMs (part 2)
Learnings + Q&A | Guest Speaker
Learnings + Q&A | Guest Speaker
Human-Centered AI & AI Governance (part 1)
Learnings + Q&A | Guest Speaker
Human-Centered AI & AI Governance (part 2)
Learnings + Q&A | Guest Speaker
When AI/ML Does Not Work
Learnings + Q&A | Guest Speaker
Final project presentations
CURRENT & PAST GUESTS
CURRENT & PAST GUESTS
Franziska Bell (bp)
Minmin Chen (Google DeepMind)
Francesca Favaro (Waymo)
Eugene Fratkin (Clari)
Christoph Kofler (Netflix)
Lance Martin (Langchain)
Chen Peng (Faire)
Rahul Roy-Chowdhuryl (Grammarly)
Jason Wei (OpenAI)
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