Artificial Intelligence | VAC | 5th/6th Semester | Problem Solving and Searching | Lecture 6

Detailed Syllabus (Artificial Intelligence - 3rd Year VAC) Unit 1: Introduction to Artificial Intelligence, Intelligent Machines and Smart Systems, Definition and Scope of AI, History and Evolution of AI, AI Problem Solving and Search Techniques, Rule-Based Systems, Natural Language Processing (introductory concepts), Computer Vision (introductory concepts) Unit 2: Machine Learning Fundamentals Introduction to Machine Learning and Data-Driven Intelligence Types of Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Concept of Training Data and Labelled Data, Regression and Classification, Clustering and Dimensionality Reduction, Neural Networks (basic idea), Decision Trees, k-NN, Deep Learning (introductory concepts), Model Training and Evaluation (accuracy, precision, recall – concept only), Real-life examples of Machine Learning applications. Unit 3: AI Applications & Tools (Stream Specific) Generic AI Applications: AI in education, Healthcare, Agriculture, Governance, Business analytics, Finance, Robotics, Media and entertainment, Hands-on Exposure to AI Tools, ChatGPT, Gemini, Copilot, Claude, Canva AI, Runway ML, Google Teachable Machine, DALL·E, Adobe Firefly, Pictory, Google AI Studio. A) AI for Arts Stream: AI in literature, Language translation, Digital humanities, Music composition, Creative writing, Journalism, Media analytics, Visual arts OR B) AI for Science Stream: AI in climate modelling, Healthcare, diagnostics, Bioinformatics, Environmental monitoring, Scientific data analysis, Research automation. OR C) AI for Commerce Stream: AI in marketing, Customer analytics, Accounting automation, Fraud detection, E-commerce, Stock prediction, Financial services. Unit 4: Ethical, Social, Economic and Legal Implications of AI, Ethical AI and Responsible AI Practices, Algorithmic Bias and Fairness, Privacy and Surveillance Issues, Misinformation and Deepfakes, Intellectual Property and AI-generated Content, AI and Employment, Human–AI Collaboration, Digital Divide, Sustainable AI, Existing Laws and Policies related to AI, Need for AI Governance and Regulation. Unit 5: Group Mini Project & Presentation: Students shall identify a real-life case study from their stream and: Design a conceptual AI-based solution, or Critically evaluate an existing AI system/tool. The project should include: Problem identification, AI application analysis, Ethical considerations, Report preparation, Seminar presentation. Teaching–Learning Methodology (Instruction for Faculties to their Students): Interactive lectures with multimedia demonstrations, Hands-on activities using no-code AI tools, Case studies and interdisciplinary discussions, Group activities, seminars and presentations, Reflective assignments and mini-projects Assessment Scheme Component Weightage Quizzes / MCQ Tests 10% Assignments / Reflective Journals 10% Class Participation & Project Work 30% End Semester Examination 50% Text Books Saptarsi Goswami, Amit Kumar Das, and Amlan Chakrabarti. AI for Everyone: A Beginner's Handbook for Artificial Intelligence (AI), Pearson. Sridhar Seshadri, Shreeram Iyer. AI for Everyone: A Common Man's Guide to Artificial Intelligence, Embassy Books. Suggested References Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, Pearson. Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido. Selected online resources and AI tool documentation.