Chapter 1: Introduction to Artificial Intelligence
1.1 What is Artificial Intelligence?
1.2 How Does AI Work?
1.3 Advantages and Disadvantages of AI
1.4 History and Evolution of AI
1.5 Types of AI: Weak AI vs Strong AI
1.6 Levels of AI:
1.6.1 Reactive Machines
1.6.2 Limited Memory
1.6.3 Theory of Mind
1.6.4 Self-Awareness
1.7 AI vs Augmented Intelligence vs Cognitive Computing
1.8 Machine Learning and Deep Learning Overview
Chapter 2: Machine Intelligence and Knowledge Representation
2.1 Defining Intelligence: Human vs Machine
2.2 Components of Intelligence
2.3 Agents and Environments in AI
2.4 Search in AI
2.4.1 Uninformed Search Algorithms
2.4.2 Informed Search Algorithms
2.4.3 Pure Heuristic Search
2.4.4 Best-First Search (Greedy Search)
2.5 Knowledge Representation
2.5.1 Types of Knowledge
2.5.2 Knowledge-Based Agents
Chapter 3: Prompt Engineering for AI
3.1 Introduction to Prompt Engineering
3.2 Evolution of Prompt Engineering
3.3 Types of Prompts
3.4 How Prompt Engineering Works
3.5 Role of Prompt Engineering in Human–AI Communication
3.6 Advantages and Challenges of Prompt Engineering
3.7 Future of LLM Communication
Subsection: Prompt Engineering Techniques
3.8 Instruction Prompt Technique
3.9 Zero-Shot Prompting
3.10 One-Shot and Few-Shot Prompting
3.11 Self-Consistency Prompting
Subsection: Prompts in Creativity and Writing
3.12 Prompts for Creative Thinking
3.13 Prompts for Effective Writing
Chapter 4: Machine Learning Fundamentals
4.1 AI Techniques and ML Overview
4.2 Machine Learning Models
4.3 Regression Analysis in ML
4.4 Classification Techniques
4.5 Clustering Techniques
4.6 Naïve Bayes Classification
4.7 Neural Networks in AI
4.8 Support Vector Machines (SVM)
Chapter 5: Trends and Ethical Concerns in AI
5.1 AI and Ethics: Challenges and Risks
5.2 AI as a Service (AIaaS)
5.3 Recent Trends in AI
5.4 Expert Systems
5.5 Internet of Things (IoT) and AI
5.6 Artificial Intelligence of Things (AIoT)
Chapter 6: Robotics and Applied AI
6.1 Robotics as an Application of AI
6.2 AI-Powered Drones
6.3 No-Code AI Platforms
6.4 Low-Code AI Platforms
Subsection: Industrial Applications of AI
6.5 AI in Healthcare
6.6 AI in Finance
6.7 AI in Retail
6.8 AI in Agriculture
6.9 AI in Education
6.10 AI in Transportation
6.11 AI in Experimentation and Multidisciplinary Research
Chapter 7: Practical Activities and Case Studies
7.1 Practical Assignments in Prompt Engineering
7.2 Case Studies in AI Applications
7.3 Ethical Prompt Design and Bias Reduction
7.4 Simulation of Customer Support Chatbots
7.5 Real-World AI Problem-Solving Activities
Chapter 8: Learning Resources
8.1 Key Textbooks and References
8.2 Recommended Online Resources and Courses
8.3 AI Tools and Platforms for Beginners
Suggested Learning Resources: (Textbook/ Reference Book/ Manuals):
Textbooks:
1.Reema Thareja, Artificial Intelligence: Beyond Classical AI, Pearson Education, 2023.
2.Ajantha Devi Vairamani and Anand Nayyar, Prompt Engineering: Empowering Communication, 1st Edition, CRC Press, Taylor & Francis Group, 2024. (DOI: https://doi.org/10.1201/9781032692319).
3.Saptarsi Goswami, Amit Kumar Das and Amlan Chakrabarti, “AI for Everyone – A Beginner’s Handbook for Artificial Intelligence”, Pearson, 2024.
Reference books / Manuals:
1.Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (4th Edition), Pearson Education, 2023.
2.Elaine Rich, Kevin Knight, and Shivashankar B. Nair, Artificial Intelligence, McGraw Hill Education.
3.Tom Taulli, Prompt Engineering for Generative AI: ChatGPT, LLMs, and Beyond, Apress, Springer Nature.
4.Nilakshi Jain, Artificial Intelligence: Making A System Intelligent, First Edition, Wiley.
Web links and Video Lectures (e-Resources):
1. Elements of AI : https://www.elementsofai.com
2. CS50’s Introduction to Artificial Intelligence with Python – Harvard: https://cs50.harvard.edu/ai/
3. Google Machine Learning Crash Course : https://developers.google.com/machine-learning/crash-course
4. Learn Prompting (Open-Source Guide) : https://learnprompting.org
5. Google AI – Learn with Google AI: https://ai.google/education/
6. Coursera – Machine Learning by Andrew Ng (Stanford University) : https://www.coursera.org/learn/machine-learning
7. OpenAI Prompt Engineering Guide (for ChatGPT) : https://platform.openai.com/docs/guides/gpt-best-practices
8. Prompt Engineering for Developers – DeepLearning.AI + OpenAI : https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
9. Ethics in AI – Google Responsible AI Practices : https://ai.google/responsibilities/responsible-ai-practices/
10. Google Teachable Machine (Train AI models visually without code) : https://teachablemachine.withgoogle.com
