AI: Realizing Opportunities
This course is aimed at employees, who have an interest in artificial intelligence and machine learning, and managers, who want to supervise data science projects.
- You will get an overview of the topic of artificial intelligence / machine learning and typical fields of application.
- You will get to know the data analysis process and understand which problems can be solved with machine learning and where difficulties can arise.
The three-day workshop follows a "flipped classroom" format, where the participants study the theory and solve the exercises at their own speed and the results are then discussed in the group sessions.
Prerequisites
- Good command of the English language (all materials are in English)
- Basic understanding of math (at high school level)
- Have fun puzzling and an interest in new technologies
- Complete the Preparation part before the first group session (about 20min; see below)
Agenda
The course consists of three group sessions and a self-study part before each group session, where you read through the chapters of the book, test yourself with short quizzes, and take notes in the workbook. Your answers to the questions in the workbook are the basis for the group discussions.
Important: Please complete the Preparation part before the first group session!
The group sessions take place remotely via Microsoft Teams, Google Meet, Zoom, Slack, or similar. Please join the calls with your camera turned on so the sessions feel a bit more personal.
How to get the most out of this course
Think of the questions in the workbook as questions an interested colleague might ask you after the course. Following the Feynman Technique, try to explain what you've learned in your own words, which is the easiest way to identify any gaps in your knowledge.
Don't try to memorize any facts, but instead connect them with what you already know and make sure you understand the "why" behind the answers.
And if anything seems confusing, please make a note of it and ask in the next meeting!
Preparation before the course (~20min)
- Read the first chapter of the book: "Introduction"
- Answer the first questions in the workbook [docx]
Day 1
Group Session (~1h)
- Introduction
- Discuss the first workbook questions from the preparation part
Self-Study Part 1 (~5h)
Block 1.1:
- Read the chapter: "The Basics"
- Again, don't forget to take notes in the workbook [docx]
- Answer Quiz 1 (also available as a pdf in case you can't access Google Forms)
- Answer Quiz 2 [pdf]
Block 1.2:
- Read the chapter: "Data Analysis & Preprocessing"
- Answer Quiz 3 [pdf]
Day 2
Group Session (~4h)
- Discuss workbook questions from the 1st self-study part
- Prepare a 90-second Spotlight presentation for one of the given ML use cases
- Spotlight presentations
- Collect ML project ideas
Self-Study Part 2 (~3h)
Block 2.1:
- [optional] Read the chapter: "Deep Learning"
- Read the chapter: "Avoiding Common Pitfalls"
- Read the chapter: "Conclusion"
- Watch the Video: "Data Science in Practice" (which includes a short recap of the theory; 33 min)
- Answer Quiz 4 [pdf]
Block 2.2:
- Complete the exercise: "Your next ML Project" (aim for a 5 minute presentation)
Day 3
Group Session (~4h)
- Discuss workbook questions from the 2nd self-study part
- Finalize your ML project idea presentation
- ML project idea presentations / Transfer Workshop
- Feedback