IBE450 Artificial Intelligence (Spring 2024)

About the course

Artificial Intelligence (AI) is profoundly changing the way we experience our daily routines, reshaping professional arenas, and adding new dimensions to leisure activities. Positioned as an entry point into the accessible world of AI, this course would provide comprehensive understanding of AI concepts and applications, covering topics such as its introduction, underlying working principles, societal uses, real-world software-based and hardware-based applications, AI frameworks, an overview of machine learning and deep learning, potential challenges, future trajectories, and ethical considerations. Additionally, participants will have the opportunity to develop mini-intelligent modules as part of the course, enhancing their practical skills in AI implementation.

The course is connected to the following study programs

• Individual Study Courses/Part- time studies
• Bachelor in IT and Digitalization

Required prerequisite knowledge

Minimum of grade ‘C’ is required in IBE 152 Introduction to Programming or equivalent, to take this course. 

 

IBE450 Artificial Intelligence in the Semester 4 (Spring semester) course will serve as a precursor for the Machine Learning course to be offered in Semester 5 (fall semester).

 

The curriculum incorporates coding exercises designed to facilitate the understanding of development of elementary AI modules. Students are expected to demonstrate a strong command of basic programming, with a preference for proficiency in Python. While the latter is considered an added advantage, a solid foundation in programming is non-negotiable for successful completion of this course.

The student's learning outcomes after completing the course

By the end of the course after having passed the examination, participants will have gained the knowledge, skills, and competence necessary to navigate the dynamic field of Artificial Intelligence, setting a solid foundation for future learning and engagement in AI-related endeavors.

Knowledge:

• Foundational Understanding: Demonstrate a comprehensive knowledge of fundamental AI concepts, including machine learning, neural networks, and algorithms.

• AI Applications: Acquire knowledge about AI applications with a focus on their societal uses, across various sectors, such as healthcare and finance, and develop insights into the implications of AI for everyday routines, understanding of the distinction between software-based and hardware-based AI applications.

 Skills:

• Programming Skills: Develop basic programming skills in Python for AI applications, enabling participants to engage with coding exercises and simple AI program development.

• Critical Thinking: Analyze and evaluate the societal impacts of AI, recognizing potential biases, ethical concerns, and challenges that may arise in the development and deployment of AI systems.

• Application Design: Apply AI concepts and principles in hands-on projects, demonstrating the ability to design and implement simple AI applications.

• Communication: Effectively communicate and engage in conversations about AI's impact on society, presenting informed perspectives on ethical considerations and potential challenges.

 Competence:

• Problem-Solving: Demonstrate competence in identifying potential challenges in AI applications and proposing solutions that align with ethical principles.

• Future Orientation: Anticipate and discuss potential future trajectories of AI, considering technological advancements, societal shifts, and ethical considerations.

• Ethical Decision-Making: Develop competence in making ethical decisions related to AI development and application, considering the broader societal implications.

• Entry-Level Proficiency: Attain a level of proficiency that allows participants to confidently enter the world of AI, engaging in discussions and further exploration of advanced AI topics.

Forms of teaching and learning

The course is entirely classroom-based, with all teaching and learning activities physically taking place. It is organized to have 2 hours of lecture and 2 hours of lab/group discussion/report writing exercise per week. During the lab exercise sessions, students will be supported by teaching assistants.

Coursework requirements - conditions for taking the exam

 

 

Coursework requirement: Capstone Report - Capstone report would essentially be an opportunity to self-reflect and showcase a comprehension.

Individual/group: Group (1-3 students)

Number of coursework requirements: 1

Required coursework requirements: 1

Presence:Presence at the lectures are not mandatory

Comment: Project report

 

Coursework requirement: Assignments

Number of work requirements: 2

Required work requirements: 2

Individual/group: Group (1-3 students)

Presence: Presence at the lectures are not mandatory

Examination

• Form of assessment: School assessment

• Proportion: 100%

• Duration: 3 hours

• Grouping: Individual

• Grading scale: Letter (A - F)

• Supported material: None

Syllabus

The current reading list for 2024 Spring can be found in Leganto