AI and Machine Learning for Beginners

(Non-Vocational )

Course Title: Introduction to Artificial Intelligence and Machine Learning
Level: Undergraduate (Open to all disciplines)
Duration: 40 hours (10 weeks, 4 hours/week)
Format: In-person or hybrid (Lectures, discussions, hands-on labs, group work)
Prerequisites: None

 

Course Description

This introductory course aims to demystify Artificial Intelligence (AI) and Machine Learning (ML) for students with no prior experience in coding or computer science. It provides a foundational understanding of key concepts, tools, applications, limitations, and societal implications of AI and ML. Through interactive discussions, real-world case studies, and guided activities, students will learn how these technologies affect the world and how to critically engage with them.

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Learning Objectives

By the end of this course, students will be able to:

1. Understand the basic concepts, history, and evolution of AI and ML.

2. Identify different types of AI and ML (supervised, unsupervised, reinforcement learning).

3. Recognize key applications of AI in daily life, industries, and society.

4. Discuss ethical, legal, and social implications of AI.

5. Gain introductory exposure to AI tools and datasets without requiring coding.

6. Evaluate current debates, media narratives, and future trends in AI.

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Weekly Breakdown

Week 1: Introduction to Artificial Intelligence

• What is AI? Historical context and evolution.

• Types of AI: Narrow vs. General AI.

• Common misconceptions and myths.

• Interactive: Identify AI around us.

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Week 2: Machine Learning Basics

• What is Machine Learning?

• Differences between AI, ML, and Data Science.

• Overview of types of ML: Supervised, Unsupervised, Reinforcement.

• Case Study: Recommender systems.

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Week 3: Data in Machine Learning

• Importance of data in AI/ML.

• Types of data: structured vs. unstructured.

• Bias in datasets and data ethics.

• Activity: Explore public datasets.

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Week 4: AI Applications in the Real World

• Healthcare, education, environment, justice, finance, and creative industries.

• Discussion: How AI impacts your field of study?

• Guest Speaker: Real-world AI practitioner.

Week 5: Algorithms and Decision-Making

• How machines learn patterns.

• Introduction to classification and prediction.

• Hands-on demo: Use of prebuilt AI tools (e.g., Teachable Machine, IBM Watson).

Week 6: Understanding Neural Networks (Conceptually)

• How do neural networks work?

• Introduction to Deep Learning.

• Visual demonstration (no math).

• Ethical implications of powerful models (e.g., facial recognition).

Week 7: Ethics and Responsibility in AI

• AI ethics frameworks (e.g., fairness, accountability, transparency).

• Case studies: Predictive policing, bias in hiring algorithms.

• Group discussion: What is ethical AI?

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Week 8: AI and the Law

• Legal frameworks for AI: privacy, consent, surveillance.

• Introduction to AI governance and global policies.

• Debate: Should AI be regulated

Week 9: Future of Work and Human-AI Interaction

• AI in the workplace: automation vs. augmentation.

• Skills for the future in an AI-integrated society.

• Interactive workshop: Design your own AI-powered social solution.

Week 10: Student Presentations + Review

• Group presentations: "AI in My World" – students present on an AI topic relevant to their field or interests.

• Course wrap-up and feedback.

• Reflection: How will AI affect your future?

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