Entrepreneurship for AI and Computer Science

2019-2020

Course Objective

After having finished this course successfully, the student is able to:
- Master the obliged literature on entrepreneurship from the course.
- Critically make use of theoretical foundations for practice-based
ideas.
- Think ‘out of the box’ concerning entrepreneurial ideas.
- Approach entrepreneurial challenges with extra confidence.
- Develop a business idea according the regular criteria.
- Thoroughly communicate the business plan in a short pitch.

Course Content

In recent years, entrepreneurship education has shifted from the
exclusive domains of business administration and economics to many other
domains as well, including beta sciences. In the academic year
2018/2019, the subject of entrepreneurship was taught for the first
time for students of the master Computer Science (joint degree of the
Vrije Universiteit Amsterdam and the University of Amsterdam) and the
master Artificial Intelligence (Vrije Universiteit Amsterdam), in the
form of this elective Entrepreneurship for AI and CS (Artificial
Intelligence and Computer Science). This elective is only for students
from these two masters.

This course is based on three pillars:
- The transfer of academic knowledge in the field of entrepreneurship,
during lectures and study of academic papers from renowned international
journals and academic books.
- The development of personal entrepreneurial soft skills, which may
contribute to entrepreneurial success, during interactive workshops.
- To come from a business idea to a solid business plan, with the help
of the adjusted Business Model Canvas approach and the study of an own
real life case.
Scientific research is not an explicit part of this course, although it
is one of the four pillars of academic entrepreneurship education.
However, scientific research will be dealt with directly and indirectly
within all three mentioned pillars of this course.

Teaching Methods

Lectures, workshops, coaching sessions, cases and small assignments.
Presence with all lectures, workshops and coaching sessions and timely
submission of the cases and the small assignments are mandatory.

Method of Assessment

The final grade is based on a team assignment (the Business Model Canvas
approach) and an individual written exam. Both the assignment and the
exam determine 50% of the final grade. Both the assignment and the exam
must be of sufficient quality.

Literature

A series of articles and one book, that will be presented in the course
manual.

General Information

Course Code XM_0009
Credits 6 EC
Period P5
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator prof. dr. E. Masurel
Examiner prof. dr. E. Masurel
Teaching Staff prof. dr. D. Iannuzzi
prof. dr. E. Masurel

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Study Group
Target audiences

This course is also available as: