ANU UG/Degree Applications of AI Complete Tutorial, Notes & Syllabus (APSCHE UG 2025-26)

 Artificial Intelligence has become a transformative force across every field of study, from agriculture and life sciences to commerce, humanities, physical sciences, and computer science. Rather than being limited to programming experts, modern AI emphasizes understanding data, using intelligent tools, and applying AI concepts to solve real world problems. This tutorial series on Applications of Artificial Intelligence is designed to introduce students from all disciplines to the AI ecosystem in a simple, practical, and application oriented manner. It focuses on how AI works in everyday systems, how data drives intelligence, and how no code and low code platforms enable anyone to build AI powered solutions.

This tutorial follows the APSCHE Skill Course syllabus and is structured to gradually guide learners from basic concepts such as AI infrastructure and data foundations to advanced applications like agriculture analytics, business intelligence, language processing, scientific discovery, cybersecurity, and workflow automation. With real life examples, practical demonstrations, and ethical discussions, the series aims to build conceptual clarity, industry awareness, and skill readiness. By the end of this tutorial, learners will be equipped to understand AI applications in their domain, use AI tools confidently, and prepare effectively for examinations, labs, and future careers.

Tutorial Index Applications of Artificial Intelligence

Based on APSCHE Skill Course Semester II

Introduction Section

1. Introduction to Artificial Intelligence
   Meaning scope and evolution of AI
   Why AI is a skill course for all disciplines
   AI in everyday life examples

2. AI Ecosystem Overview
   Hardware software data and people
   Role of AI in modern education and industry

Module 1 Infrastructure and Platforms for AI Applications

3. AI Hardware Fundamentals
   CPU GPU TPU NPU explained simply
   Memory RAM VRAM and storage types
   Why GPUs matter in AI

4. AI Platforms for Application Development
   Online AI platforms overview
   Google AutoML H2O AI Teachable Machine
   Desktop no code and low code tools
   Orange KNIME Weka RapidMiner

5. Edge AI Concepts and Applications
   What is Edge AI
   Edge AI vs Cloud AI
   Examples in smart appliances
   Smart cameras vehicles and IoT devices

Module 2 Foundations of Data for AI

6. Importance of Data in AI
   Data as fuel for AI
   Role of big data in training AI models

7. Data Information and Knowledge
   Difference between data information and knowledge
   Real world examples

8. Types and Structure of Data
   Structured semi structured and unstructured data
   Text image audio video tabular time series spatial data

9. Data Formats Used in AI
   CSV JSON XML
   Image formats JPEG PNG
   Audio and video formats

10. Public and Private Datasets
    What are public datasets
    Importance of open data
    Popular repositories
    Kaggle
    Hugging Face
    UCI Repository
    Google Dataset Search
    Data licensing basics

11. Ethics Privacy and Responsible AI
    Why ethics matter in AI
    Data privacy issues
    Overview of GDPR and HIPAA
    Responsible AI practices

Module 3 AI Data Pipeline

12. AI Data Pipeline Overview
    Stages of AI pipeline
    From data collection to model readiness

13. Data Collection Methods
    Manual data collection
    Sensors and IoT data
    Web scraping
    APIs and system logs

14. Data Annotation and Labeling
    What is data annotation
    Manual vs automated annotation
    Types of annotation
    Classification
    Bounding boxes
    Segmentation
    NER

15. Data Cleaning and Preprocessing
    What is dirty data
    Missing values duplicates outliers noise
    Data cleaning steps
    Importance of preprocessing

16. Data Splitting and Transformation
    Training and testing data
    Normalization and feature engineering concept

Module 4 Domain Specific Applications of AI

Life Sciences Agriculture and Environment

17. AI in Agriculture
    Plant disease detection
    Crop yield prediction
    Precision agriculture

18. AI in Zoology Ecology and Environment
    Wildlife monitoring
    Aquatic systems
    Pollution and forest analysis

19. AI in Biotechnology and Chemistry
    Genome sequencing
    Protein structure prediction AlphaFold
    Drug discovery and chemical prediction

Commerce and Management

20. AI in Commerce
    Recommendation systems
    Chatbots and virtual assistants
    Sentiment analysis
    Demand forecasting

21. AI in Business Operations
    Fraud detection
    HR analytics
    Supply chain optimization
    Explainable AI in business

Humanities and Social Sciences

22. AI in Economics and Public Policy
    Market trend prediction
    Social media analysis

23. AI in Languages Literature and Arts
    Machine translation
    Text summarization
    AI assisted creative writing
    AI art and music generation

24. AI in Society
    Bias fairness and transparency
    Impact of AI on jobs and democracy

Physical Sciences and Mathematics

25. AI in Physics and Chemistry
    Astronomy image analysis
    Material science discovery
    Energy optimization

26. AI in Mathematics and Earth Sciences
    Pattern recognition
    Optimization problems
    Climate modeling
    Remote sensing

Computer Science and Cyber Security

27. No Code and Low Code AI Development
    Concept of vibe coding
    Prompt driven development
    Popular tools overview

28. Workflow Automation Using AI
    What is automation
    Tools like Zapier Power Automate n8n
    Real world automation examples

29. AI in Cyber Security and Networks
    Intrusion detection
    Network traffic prediction
    Digital forensics using AI