Course Catalog Description:

This introductory course provides preparatory year students with foundational knowledge in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Students will explore core AI concepts and get hands-on experience with no code platforms to build AI models. By the end of the course, students will understand key AI concepts, apply basic model training and testing techniques, explore real-world applications of AI, and discover ethical implications of AI.  

Course Objectives:

1. Introduce fundamental concepts of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).
2. Equip students with hands-on experience of using AI models and improve problem-solving skills.
3. Design effective prompts for Large Language Models (LLMs) and analyze model responses.
4. Evaluate ethical and societal implications of AI solutions.

Course Learning Outcomes:

Upon successful completion of this course, the student should be able to:

1. Explain the key concepts of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).
2. Differentiate between supervised, unsupervised, and reinforcement learning techniques.
3. Use no-code AI tools for implementing basic AI Models.
4. Apply prompt‑engineering techniques to improve LLMs outputs.
5. Identify bias, fairness, privacy, and safety concerns in AI solutions.
6. Demonstrate discipline, and teamwork in developing and evaluating AI solutions.

Required Material:

• Course material will be provided on the blackboard in the form of Lecture Slides and Practice Exercises.
• No-code AI tools, e.g., Teachable Machine, KNIME Analytics, MATLAB, LLMs, will be used for LAB experiments and projects.
• Lectures / Labs for the course will be conducted face-to-face on campus. As a backup, Blackboard Collaborate Ultra or MS Teams will be used.