Instructor: Dr. Marios D. Dikaiakos,
Professor Teaching Assistant: ECTS Credits: 8 Semester: Spring Academic Year: 2023-2024 Course Level: Post-graduate. Course Type: Compulsory course for the M.Sc. in Artificial Intelligence; Advanced
Elective for the M.Sc. in Data Science, M.Sc. in Computer Science, Professional M.Sc. in Advanced
Information
Technologies, and Ph.D. of the University of Cyprus. Free elective for other post-graduate students of other
programs. Prerequisites: None. Language of instruction: English. Online Forum:Slack Class Scheduling and Assignments:Sharepoint Teaching Schedule: Three hours of lectures and one hour of recitation per week.
Wednesdays: when there are seminars by the
Centre for Entrepreneurship consult
their News and Events
for time and place. Otherwise, the recitation will be held on 15:00-15:59, in B101 Lab, ΘΕΕ01.
Objectives
This course aspires to help students explore and master key concepts and challenges of relevance to AI and
Data-driven entrepreneurship. The course introduces students to the world of AI entrepreneurship through
case studies that demonstrate successes, failures and challenges. The course provides also an overview of
and an introduction to key steps to develop a company, design a business model, explore product-market fit,
manage intellectual property, and attract investment. Students will explore acknowledged innovation-driven
entrepreneurship methodologies and experiment with them and associated tools to pursue the translation of
their ideas into entrepreneurial endeavors. The course examines issues faced by Startup Founders and Chief
Technology Officers who need to innovate at the boundaries of AI, Ιnformation Τechnology and Βusiness by
understanding all perspectives.
Learning outcomes and skills
The students who complete this course successfully, will be able to:
Recognize and define key concepts and terminology related to entrepreneurship.
Analyze and evaluate entrepreneurial ideas, especially for AI-based, innovative products, processes
or services based on advanced technologies or scientific inventions.
Consider issues of Intellectual Property (IP) and IP protection.
Understand Business Planning and create Business Plans.
Define and apply techniques for market analysis, product design, value proposition definition,
and customer acquisition.
Explore and apply methodologies and tools for innovative entrepreneurship, such as the
Disciplined Entrepreneurship Methodology, the Lean Product Methodology, and the Business Model
Canvas.
Explore and apply techniques for creative ideation and design of software applications, products and
services, such as Design Thinking, Innovators’ Compass, and Sprint.
Understand the challenges of team formation and management.
Use state-of-the-art collaboration, ideation and rapid prototyping tools.
Prepare pitch-decks and present ideas in front of investors to attract funding.
Understand and explain the interplay between Big Data, Machine Learning and various application
domains.
Recognize and undertake the steps of the Disciplined Entrepreneurship methodology, and manage the
key activities required
to bring an innovative product or service to the market:
product definition and market segmentation;
value proposition analysis and high-level product specification;
market and competition analysis;
business model definition and revenue models;
customer and user acquisition;
pricing strategies for a product, process or service;
minimum viable product definition and product implementation planning.
Understand the basics of fundraising, financing, and ownership for a startup.
Understand the key challenges for attracting talent, establishing and managing a startup team.
Prepare pitch decks, and pitch in front of potential investors, an ΑΙ-related business
idea/product/service.
Teaching and Learning Methods
Students will meet the expected learning outcomes through participation to lectures, invited talks,
active participation to class discussions, reviewing videos, reading and writing assignments, and actual
practice with innovation and entrepreneurship methodologies. The course comprises 3 hours of weekly
lectures, 1 hour of recitation, reading and writing assignments, and a semester-long group project in
entrepreneurship.
Participation of lectures and recitation is obligatory.
In summary, the teaching and learning methods are the following:
Active participation to Lectures, where the main concepts and methodologies covered
by the
course are presented and critically appraised. Students are required to review and study the
materials for
each lecture, as defined in the Course Topics, and
actively participate in class.
Participation to Recitation, where students will expand on topics covered in the
Lectures through:
Guest Lectures and Seminars, which are part of the Series of Lectures in Innovation and Entrepreneurship
(KEP101) of the Centre for Entrepreneurship, and bring to campus distinguished
entrepreneurs and
experts in various aspects of relevance to entrepreneurship.
Open discussion, student presentations, and viewing and discussing of relevant online
material.
Individual case study and report, where each student is expected to read and work
creatively on a
case study of their choice from a book assigned by the professor.
Group projects, where each team of students is expected to develop an AI-related
idea with a
strong exploitation potential through a business venture or a social enterprise. The teams are
required to undertake all necessary activities to develop a strong business plan, prepare a final
oral presentation to seek funding (Venture Capital pitch), and present their pitch in class and at
the
Student Innovators Competition (SINN 2023) and/or other competitions approved by the professor.
More information on assignments and group projects is available in the assignments
web page and will be updated through announcements in Sharepoint and/or
Slack.
Evaluation and Grading
Student progress is evaluated continuously through class participation and the assessment of
writing assignments and group project deliverables. The final grade is based on the following formula:
C4E and Guest Seminars' Reports:
5%
Class Participation:
5%
Case Study Report:
20%
Term Project Reports:
50%
Term Project Pitching Presentation:
20%
ECTS Analysis
One ECTS unit corresponds to 25-30 hours of work undertaken by an average student to
complete successfully expected learning outcomes. Consequently, the successful completion of the class
requires a total of 187.5-225 hours of work, on average. The workload of the average
student is analysed as follows:
Class and Recitation participation: 4 hours per week for 13 weeks, totalling to 52
hours.
Study at home: 2 hours per week for 14 weeks, totalling 28 hours.
Case Study Report: 40 hours.
Final presentation: 10 hours.
Group project: 70 hours
Consequently, the total workload for successfully completing this course, is estimated to 205 hours on
average.
Bibliography
Bill Aulet, “Disciplined Entrepreneurship.” Wiley, 2013.
Alexander Osterwalder and Yves Pigneur, “Business Model Generation.”
Wiley, 2010.
Kai-Fu Lee and Chen Qiufan, "AI 2041: Ten Visions for Our Future." Currency, 2021.
Dan Olsen, “The Lean Product Playbook. How to Innovate with Minimum Viable Products and Rapid Customer
Feedback.” Wiley, 2015.
Ash Fontana, "The AI-First Company: How to Compete and Win with Artificial Intelligence." Penguin, 2021.
The field of Artificial Intelligence (AI) is undergoing rapid developments. Generative Artificial Intelligence
tools, such as ChatGPT/ChatPDF,
allow the generation of written text, audio and images with highly realistic results. The University of Cyprus
(UCY) is committed in the
ethical and responsible use of AI based on specific principles for its utilization and on the appropriate
preparedness of its staff and
students. The following principles/recommendations regarding the utilization of AI in the educational process
are in effect, starting in the Fall Semester 2023-2024, and will be revised at regular time intervals.
GENERAL CONTEXT OF USE
UCY encourages experimentation with available AI tools that aid the process of comprehension and
learning,
given that their use does not violate applicable ethical principles. It is noted that, AI tools must be
utilised in combination
with critical thinking, while also acknowledging their sometimes potentially serious weaknesses.
Implementation of such
practices, can potentially lead to the constructive enhancement of the teaching and learning process.
Instructors may allow students to use AI tools in coursework assignments for the purpose of searching
and
cross-referencing information, to the extent that such use contributes to the achievement of the
learning objectives.
TRANSPARENCY AND QUALITY ASSURANCE
When AI tools are used in coursework assignments, students must explicitly and accurately declare any AI
tools used and how these were utilized in the process.
It is noted that the use of AI tools by students can contribute to the cultivation of critical thinking
skills, through the evaluation of the quality of the assistance/responses generated by the AI tool(s).
Specific instructions for the use of AI by students may be provided by the instructors as part of the
course syllabus or as part of the written guidelines of a coursework assignment/examination, based on
the unique characteristics of a course.
Instructors are encouraged to adapt the assessment methods for coursework assignments/examinations in
order to ensure the integrity of the assessment process (e.g., take-home exams and thesis assessments
may be supplemented by an oral examination).
ETHICAL USE AND ACCOUNTABILITY
Verbatim copying from text generated by AI tools is prohibited. Instructors have the option of using
plagiarism detection tools that recognize AI-generated text. It is
noted, however, that plagiarism detection tools may have a margin of error. In the event of plagiarism,
further disciplinary action may be taken in accordance with the
Undergraduate
Studies Rules (pages 35-43) for undergraduate students and with the Postgraduate Studies Rules
(pages 48-57) for
postgraduate students
(Additional information on how students can recognize and avoid plagiarism in general, are provided in
the electronic
booklet guide of the University of Cyprus Library entitled "ACADEMIC PLAGIARISM: How to identify and
avoid it”.)
It is noted that AI tools should be utilized with great caution, with regards to the data submitted to
them as input (personal or confidential data, copyrighted information, etc.) and to the results that
arise from their use. It is also important to consider that, currently popular AI tools generate
algorithmically-derived responses based on existing text which is available online, without confirmation
of the accuracy and timeliness of these responses in comparison to valid scientific information sources.
Users of AI tools are solely responsible for confirming the accuracy of data and ensuring unbiased and
ethical conduct.
Other instructions
Each student has the right to attend lectures and workshops without disturbance and
unjustified interruptions. So everyone is requested to preserve this right, respecting the start
and end time of the lectures and workshops, not disturbing the class, and preserving the cleanliness
of the auditoriums and laboratory spaces and in general the academic freedom.
Plagiarism is strongly prohibited.
You will not get any credit for late submissions. We will grant extensions only in the case of
illness (with a doctor's note) or extraordinary circumstances. Please let us know ahead of time if
illness or
an extraordinary circumstance will cause you to submit a writeup or paper late, then you should
discuss the
matter with your instructor as soon as possible.