Introduction to AI

No prerequisites

1. Meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence
a. Fundamental concepts of Machine Learning and Deep Learning
2. Difference between supervised, semi-supervised and unsupervised learning
a. Machine Learning workflow and how to implement the steps effectively
3. The role of performance metrics and how to identify their essential methods

1. Lesson 1 - Decoding Artificial Intelligence
2. Lesson 2 - Fundamentals of Machine Learning and Deep Learning
3. Lesson 3 - Machine Learning Workflow
4. Lesson 4 - Performance Metrics

Learning Outcomes:

On successful completion of the course, the candidate will be able to:

1. Meaning, purpose, scope, stages, applications, and effects of Artificial Intelligence

2. Fundamental concepts of Machine Learning and Deep Learning

3. Difference between supervised, semi-supervised, and unsupervised learning

4. Machine Learning workflow and how to implement the steps effectively.

5. The role of performance metrics and how to identify their essential methods

The online delivery blends synchronous and asynchronous components. Students complete self-directed assignments hosted on the course platform. Weekly Forums support a required active, rubric-based student contributions that foster collaboration.

Exams may include essays, short answers, or MCQs. Formats include open/closed book. Time zones are considered. Assessments are formative and summative. Students have 2 weeks to review grades and must follow the appeal process in the Student Handbook.

Certificate issued on the Blockchain.

Please note that there are three semesters as intake periods as noted on the academic calendar.
Scholarship Awarded (Total Fee: €100)

Registration is done online.