Introduction to Artificial Intelligence (AI Fundamentals) – Complete Tutorial, Notes & Syllabus (APSCHE UG 2025-26)

 Introduction to Artificial Intelligence (AI Fundamentals)

Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century, shaping how we live, learn, and work. The “Introduction to Artificial Intelligence (AI Fundamentals)” course is designed especially for undergraduate students to build a strong foundation in the core concepts, applications, and ethical dimensions of AI.

This course introduces students to the history, evolution, and subfields of AI — including Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Robotics, and Knowledge Engineering. It explains how AI systems mimic human intelligence through perception, reasoning, and decision-making.

Learners will explore real-world AI applications across key sectors like healthcare, agriculture, education, finance, and transportation. Beyond technical knowledge, the course emphasizes responsible AI development, addressing crucial topics such as bias, fairness, transparency, privacy, inclusivity, and sustainability in AI systems.

A unique feature of this syllabus is the integration of Generative AI and Prompt Engineering, enabling students to experiment with tools like ChatGPT, Gemini, Hugging Face, and SlidesGPT for content generation, creative design, and interactive learning. These hands-on exercises make the course both engaging and industry-relevant.

By the end of this course, students will:

• Understand the fundamental concepts and subfields of Artificial Intelligence.
• Analyze how AI impacts diverse industries and daily life.
• Evaluate ethical and societal challenges of AI.
• Gain practical exposure to Generative AI and Prompt Engineering.
• Develop confidence to apply AI tools for innovation and research.

Whether you’re an aspiring data scientist, a software engineer, or simply curious about how AI works, this course will help you think critically, experiment creatively, and understand the power of intelligent systems.

The three units given in the syllabus is divided in five units for easy understing of students.

Unit 1: AI and Its Subfields

Overview: Introduction to AI, historical evolution, definitions, challenges, and subfields.

1. Introduction to Artificial Intelligence – Definition, Scope & Goals

2. History of Artificial Intelligence: From Turing to ChatGPT

3. Types of AI: Narrow AI vs General AI vs Super AI

4. Major Subfields of AI Explained:

   • Knowledge Engineering

   • Machine Learning (ML)

   • Computer Vision (CV)

   • Natural Language Processing (NLP)

   • Robotics

5. Industry Applications of AI – Healthcare, Agriculture, Education & Beyond

6. Challenges in Building Intelligent Systems – Data, Ethics, and Resources

7. Case Study: How AI is Powering Smart Cities and Digital India

Unit 2: Applications of AI

Overview: Real-world impact of AI across sectors.

1. AI in Healthcare – Early Diagnosis & Drug Discovery

2. AI in Finance – Fraud Detection, Risk Analysis, and Algorithmic Trading

3. AI in Retail – Recommendation Systems and Customer Analytics

4. AI in Agriculture – Crop Prediction and Smart Irrigation

5. AI in Education – Personalized Learning and Virtual Tutors

6. AI in Transportation – Autonomous Vehicles and Traffic Management

7. Future of AI Applications – Quantum AI and Edge Computing

Unit 3: Bias, Fairness, and Ethics in AI Systems

Overview: Understanding the social, ethical, and governance aspects of AI.

1. What Is AI Ethics? Principles Every AI Developer Should Know

2. Understanding Bias in AI – Causes and Consequences

3. Fairness in AI Systems – Building Trustworthy Models

4. Transparency and Explainability in AI Decisions

5. Accountability and Privacy in Machine Learning Models

6. Inclusivity and Sustainability in AI Development

7. Robustness and Reliability – Making AI Systems Safe

Tutorial Add-Ons:

• Examples of biased outputs from ChatGPT, DALL-E, etc.

• Classroom activity: Detecting gender bias in AI image generation

Unit 4: AI in Research, Generative AI & Prompt Engineering

Overview: Exploring Generative AI tools and their research relevance.

1. Role of AI in Modern Scientific Research and Experimentation

2. What Is Generative AI? Understanding ChatGPT, Gemini, and Hugging Face

3. Generative AI vs Traditional AI – Key Differences Explained

4. Introduction to Prompt Engineering – The Art of Talking to AI

5. Prompt Design Strategies: Zero-Shot, Few-Shot, and Chain-of-Thought

6. Future Trends in AI Research – Multimodal and Federated AI

7. Hands-on Tutorial: Writing Effective Prompts for Better AI Results

Unit 5: Applications of Prompt Engineering

Overview: Practical use-cases of prompt engineering in education, business, and creative domains.

1. Prompt Engineering in Education – Smart Content Creation & Tutoring

2. How Businesses Use Prompt Engineering for Marketing and Automation

3. AI for Creative Writing – Storytelling, Poetry, and Idea Generation

4. AI for Design and Branding – Using Canva Magic Media & Adobe Firefly

5. Writing YouTube Video Scripts with AI Tools

6. Creating PowerPoint Presentations with SlidesGPT and Tome AI

7. Designing Thumbnails and Visuals with Generative AI

8. Prompt Engineering for Students – Learn by Doing