MAI622: AI Entrepreneurship

Syllabus

MAI622: AI Entrepreneurship - Syllabus

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.
Lectures:
Thursday,15:00-17:59, B101 Lab, ΘΕΕ01.
Recitation:
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:
  1. Recognize and define key concepts and terminology related to entrepreneurship.
  2. Analyze and evaluate entrepreneurial ideas, especially for AI-based, innovative products, processes or services based on advanced technologies or scientific inventions.
  3. Consider issues of Intellectual Property (IP) and IP protection.
  4. Understand Business Planning and create Business Plans.
  5. Define and apply techniques for market analysis, product design, value proposition definition, and customer acquisition.
  6. Explore and apply methodologies and tools for innovative entrepreneurship, such as the Disciplined Entrepreneurship Methodology, the Lean Product Methodology, and the Business Model Canvas.
  7. Explore and apply techniques for creative ideation and design of software applications, products and services, such as Design Thinking, Innovators’ Compass, and Sprint.
  8. Understand the challenges of team formation and management.
  9. Use state-of-the-art collaboration, ideation and rapid prototyping tools.
  10. Prepare pitch-decks and present ideas in front of investors to attract funding.
  11. Understand and explain the interplay between Big Data, Machine Learning and various application domains.
  12. 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.
  13. Understand the basics of fundraising, financing, and ownership for a startup.
  14. Understand the key challenges for attracting talent, establishing and managing a startup team.
  15. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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:
  1. Class and Recitation participation: 4 hours per week for 13 weeks, totalling to 52 hours.
  2. Study at home: 2 hours per week for 14 weeks, totalling 28 hours.
  3. Case Study Report: 40 hours.
  4. Final presentation: 10 hours.
  5. Group project: 70 hours
Consequently, the total workload for successfully completing this course, is estimated to 205 hours on average.

Bibliography

On the use of Artificial Intelligence Tools

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
TRANSPARENCY AND QUALITY ASSURANCE
ETHICAL USE AND ACCOUNTABILITY

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.