Pieter Abbeel, Zsolt Katona and Matthew Stepka - The Business of AI

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Syllabus - The Business of AI (EWMBA267-11 - Spring 2020)

Note: this document may be updated in the future. Please check back for the latest version.
Last Update: January 15, 2020

Dates, Times, Location

2/9: 9am-5pm, N300

2/16: 9am-5pm, N300

Instructors - Pieter Abeel, Zsolt Katona and Matthew Stepka

Email: pabbeel@berkeley.edu, zskatona@berkeley.edu and mstepka@berkeley.edu - please use [EWMBA267] in the subject.
Office hours: by appointment - in person or via phone/skpye/hangouts


From self driving cars to humanoid robots, Artificial Intelligence (AI) is here and changing the way we live, work and do business. The class is designed to introduce future managers to AI technology and its many business applications. Students will walk away with a foundational understanding of AI and its near and long term applications, explore the myths and realities surrounding the technology, and delve into the legal, social and policy implications of AI. Professor Katona will be joined by Haas Executive-in-Residence Matthew Stepka, to provide valuable insight into the current AI business landscape.

Pieter Abbeel is Professor at the Department of Electrical Engineering and Computer Sciences and Director of the Robot Learning Lab at UC Berkeley [2008- ] and Co-Director of the Berkeley AI Research (BAIR) Lab. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation.

Zsolt Katona is the Cheryl and Christian Valentine Associate Professor of Marketing and has been at Haas since 2008. Zsolt has a Phd in Marketing and Computer Science. His research focuses on the several topics related to business analytics such complex networks, search advertising, network economics, social media and digital marketing.

Matthew Stepka is Executive-in-Residence and Lecturer at Haas. He is Managing Partner of Machina Ventures, an investment firm focused on early stage, artificial intelligence and data science enabled companies. Previously, Matthew was VP, Special Projects at Google, where he led and incubated strategic initiatives, especially mission-driven projects with high social impact.

Preparation and Participation

The success of this course depends on everybody's effort to prepare before class. Learning is the most effective when you are actively engaged in thinking about and doing rather than passively absorbing the material. Everybody's contribution is important. It is rare that there is one right answer to a particular issue. Do not expect one. Rather, you should expect to learn from seeing how others address the problem that you have thought seriously about. The better prepared you are the more you will learn. Therefore, we will try to call on everyone, not just volunteers to contribute and defend their viewpoints. Lectures and cases will be interactive.


Required Readings

For each day, please read the case study(ies) listed under sessions before class. These are required readings. You can access the case studies via study.net.


We do not use a textbook in the class. If you want to learn more, we recommend these two:


Deep Learning Homework (Individual)

You will learn to code deep learning yourself! Don't worry you don't have to write code, rather you will be given a step by step guide and the homework will involve changing bits of the code to experiment with a classification task. Details will be available on bCourses after the 2/9 session and you will have until Saturday night, 2/15 to complete the assignment.

The assignment will be run on Haas' Jupyterhub platform. The coding language will be Python and we will use the Tensorflow and Keras libraries. Learning/knowing python is not a prerequisite, but it is highly recommended that you complete the Jupyterhub Python tutorial before our first session.

To access the tutorial, go to https://jupyterhub.haas.berkeley.edu/ and click on Haas Tutorials. After you log in with your calnet account, click on "Start Server" and then click on the Python-Tutorials folder. Then click on the 1_Start_Here folder and the mp4 video file will walk you through the rest. The tutorial contains problem sets from the Data and Decisions class and goes through the solutions in Python. Completing the tutorial will give you a basic understanding of Python and the Jupyterhub platform, both of which are widely used by data scientist.

Due date: 2/15 11pm: Submit deliverable on bCourses

Online Quiz (Individual)

There will be a short multiple-choice quiz that you need to complete after our second meeting. The quiz will assess your knowledge of the most important points we learn in class. The quiz will be open book, but timed. You can start any time before the due date, but obviously do not discuss with anyone else.

Due date: 3/8 11pm: Take on bCourses

AI Application Business Plan (Group)

In this project, you will develop a plan for an AI application. Your final report should be a business plan much like you would make a pitch as the founders of a company to a venture capital investor. In addition to the instructors, a group of VC representatives who will attend our first session will provide feedback on your final submission. To facilitate their review, you will be asked to create and submit a 2-3-minute video pitching your idea. The format of the final report should be: PowerPoint deck with 15-20 slides including Appendix. Write out speaker notes as if you were presenting.

Click here for a detailed description of the project

Due date: 2/15 11pm: Submit a document containing

  1. Names of the members of your group
  2. The Title of your project (you can change this later if needed)
  3. A brief, one-paragraph description of the AI application idea that your group wishes to pursue

Due date: 3/8 11pm: Submit the final report.

Submission of Documents

Submit all the assignments on bcourses in .pptx, .pdf format or as a link (to your video or a Google slides presentation), no paper submission is necessary. If there is a problem with bCourses, email me a copy before the deadline. Your slides should be self-explanatory as much as possible, but you can add presentation notes if you need to explain something in more detail. Once project groups are assigned bCourses will have a feature for groups to submit assignments. Until then, one submission per group is enough. If multiple members submit from a group, the last submission will be graded.


Your grade will be based on the number of points you get from the following assignments. The maximum total score is 100 points.
  • Deep Learning Homework: 25 points
  • Online Quiz: 15 points
  • Group Project: 60 points
Note that Haas policies require the average GPA in elective classes to not exceed 3.5.

Session Details

2/9 - Morning: 9:00am - 1:00pm. Introduction: AI and what's behind it


  • What is AI? How does AI work?
  • Supervised Learning and Neural Networks
  • Deep Learning
  • Computer Vision Basics

2/9 - Afternoon: 2pm - 5:00pm. AI Applications


  • Where is it useful? What are the limits, limiting factors?
  • From idea to application

Case Discussion: "Zebra Medical Vision"

VC Panel

  • Richard Ling, Rembrandt Venture Partners (formerly)
  • Alastair Trueger, Creative Ventures
  • Fang Yuan, Baidu Ventures
  • Stephanie Zhan, Sequoia

Optional Readings:

2/16 - Morning: 9:00am - 12:00pm. AI Applications


  • Deep Learning Homework Debrief
  • Robotics and Reinforcement Learning
  • AI and Automation

Case Discussion: "Osaro: Picking the Best Path"

2/16 - Afternoon: 1:00pm - 5:00pm. AI Strategy, AI and the Future of Humans


  • AI and Business Strategy, Competitive Advantage
  • Future Applications of AI and the Startup Space>
  • Artifical and Human Intelligence Working Together
  • Policy Considerations: Algorithmic Bias

Optional Readings: