Important. Unfortunately this course has to be postponed. It is not clear when this course will take place in the future. The course was originally planed as block course from 28.9.2020 until 9.10.2020 at Mathematikon B and SRH Heidelberg .
Information. In CS Department at Uni HD this course will be offered as a FÜK (Fach übergreifender Kurs)
What is the way from identifying a potential market need, until planning and executing a business idea in the area of AI? This is the topic of the block course. The course is split in 4 parts. The first core part covers aspects of AI from a high-level and application perspective, such as deep learning, computer vision, hardware design, and connection of AI to related fields such as medicine and robotics. The second core part considers business aspects to launch a StartUp. This includes various Business Models, financial planning, business development, e.g. Value Proposition Canvas. The third part is dedicated to short presentations of existing start-ups. The last part consists of hands-on sessions and a final project where you should come up with your own AI-based startup. The block course is in general characterized by high interactivity and workshop character.
Content:
This course is split into four parts:
- Technical part. Short introduction to machine learning. Discussing the different areas in AI, especially machine learning and computer vision, such as deep neural networks, reinforcement learning, active learning, and unsupervised learning. Presenting the state of the art in computer vision and machine learning, such as object recognition, motion estimation, and domain adaption. State of the art in hardware design especially camera design. Discussing the connection of Machine learning and computer vision with related fields such as biology, medicine and robotics.
- Business part. This part will provide the development from problem to solution using Design Thinking bridging to Business Model Innovation where the idea is formed, streamlined and scaled into a monetizable business idea. We will cover elements like Elevator Pitch, Story Telling, Team Introduction, Business Model (Core Business including Value Proposition, Customer Segment, Customer Relationship and Channels combined with Key Partners, Key Activities and Key Recourses as well as Revenue Streams and Cost Structure), Competition, Market Entry and Closing with Call-to-Action will be processed during the course.
- We will Invite AI-based start-ups to talk about their expertise
- There will hands-on sessions and a final project where you should come up with your own AI-based startup.
Formalities:
Teaching assistant (main point of contact): Radek Mackowiak
Registration: Registration will be done via Müsli (tba)
Prerequisite: No prerequisites
Exam: There will be no marks. In order to pass the course, the students must attend the course and pass the practical project.
Leistungspunkte: 4 LP for Computer Science. Physics different.
Amount of work: 120h thereof 30h Lectures, 45h Exercises and mini project, 45h preparation and presenting of final project
Usability: Physics, MSc. Angewandte Informatik MSc. Scientific Computing
Slides
All the slides will be uploaded to heiBOX.
The password will be disclosed in the lecture.
Teaching goals:
The students:
- Understand the process of Business Model Innovation to bring an idea to a monetizable business level including a financial planning. To execute and implement business ideas through Business Development using methods like Value Proposition Canvas, Business Model Canvas, Strategic Innovation Canvas and through Business Analysis using methods like SWOT, PEST and Balanced Scorecard.
- Understand techniques for business problem-solving in the areas of ideation, prototyping and testing. Ideation based on problem definition, following rapid prototyping using different tools like LEGO, 3D-Printing and Software Mock Ups, and to get feedback through testing like split tests and iterative customer interviews.
- Understand how to present a business idea to motivate customers, supporters, multiplicators, partners and investors through a meaningful pitch deck and agile business plan. An understanding of the framework EXIST Idea Paper will help to apply for potential future funding.
- Understand user-centric problem identification such as Design Thinking. Observation of customer needs through interviews and persona creation as well as point-of-view definitions will help to prioritize relevant problem fields.
- Understand the basic principles of machine learning and computer vision, such as deep neural networks, necessary to launch a start-up as a business person.
- Understand the high-level concepts of the different fields of machine learning, such as reinforcement learning, active learning, supervised and unsupervised learning.
- Understand the state-of-the art of computer vision and machine learning, such as object recognition and motion estimation, in order to create ideas for a business model.
- Understand the application and connection of machine learning and computer vision techniques to related fields such as hardware design, camera design, robotics, medicine and biology.
Information about when and where the course takes place, can be found here: https://hci.iwr.uni-heidelberg.de/teaching