Stanford Root

Schedule

Stanford Root

Schedule

MGTECON 600

Microeconomic Analysis I

UNITS:4
GRADING:GSB Student Option LTR/PF
LEVEL:Graduate
GER:—

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 and entrepreneurs to better communicate with technical people on the team. We will start with 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, generative AI, AI governance and human-centered AI, with technical discussions on multi-objective optimization, deep learning and reinforcement learning, and large language models (LLMs). There will also be guest lecturers from different industries to provide additional insights and real-world examples. This course is recommended for MBA 2 and MSx students who have already completed Data and Decisions, and MBA 1's with a strong statistical background.

Syllabus for selected term:
View Autumn 2026 Syllabus

Sections

1 Term
Case Study 1Open
ID: 26510
0 / 20 enrolled
DAYS:Tuesday, Thursday
TIME:9:30 AM – 11:20 AM
LOCATION:GSB Patterson 101
INSTRUCTOR:
Sugaya, Takuo
4units

MGTECON 600: Microeconomic Analysis I

4 units · GSB Student Option LTR/PF

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 and entrepreneurs to better communicate with technical people on the team. We will start with 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, generative AI, AI governance and human-centered AI, with technical discussions on multi-objective optimization, deep learning and reinforcement learning, and large language models (LLMs). There will also be guest lecturers from different industries 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 MBA1's with a strong statistical background.

Offered in Autumn 2026 at Stanford University.

Autumn 2026 sections

  • Case Study — Tuesday Thursday 9:30 AM – 11:20 AM — GSB Patterson 101 — Sugaya, Takuo (Graduate)

More MGTECON courses

  • MGTECON 308: Ten Big Questions in Economics - Management Foundations
  • MGTECON 328: Economics of the Media, Entertainment, and Communications Sector
  • MGTECON 331: Health Law: Finance and Insurance
  • MGTECON 349: Designing Economic Mechanisms: Theory and Practice
  • MGTECON 533: Economics of Strategy and Organization
  • MGTECON 583: Measuring Impact in Business and Social Enterprise
  • MGTECON 601: Microeconomic Analysis II
  • MGTECON 602: Auctions, Bargaining, and Pricing
  • MGTECON 603: Econometric Methods I
  • MGTECON 604: Econometric Methods II
  • MGTECON 607: Methods for Applied Econometrics
  • MGTECON 608: Multiperson Decision Theory

All MGTECON courses · All departments