Asha Kanini

Artificial Intelligence Course Curriculum

S.No Topic Subtopic Detail/Content
1 Foundations of Artificial Intelligence 1.1 Introduction to AI Understand what AI is, its applications in daily life, and how it differs from human thinking.
1.2 Pattern Recognition Learn how AI identifies patterns and makes predictions from data.
2 AI in Different Media 2.1 Image Processing Understand how AI recognises and generates images.
2.2 Audio Processing Learn how AI processes and generates sound.
2.3 Video Processing Understand how AI predicts and generates video sequences.
2.4 Object Detection Learn how AI detects and identifies objects in images.
3 Data and Learning in AI 3.1 Training Data Understand how AI models are trained using data.
3.2 Bias and Fairness Learn how bias occurs in AI systems and its impact.
3.3 Representation of Data Understand tokens, embeddings, and how AI represents information.
3.4 Neural Networks Learn basic concepts of neural networks and attention mechanisms.
4 Ethical and Responsible AI 4.1 Responsible Use Understand ethical considerations and safe use of AI.
4.2 Validation of Outputs Learn to verify and question AI-generated results.
5 Programming Fundamentals 5.1 Variables and Data Types Learn how data is stored and used in programs.
5.2 Operators and Expressions Understand arithmetic and logical operations.
5.3 Control Structures Use conditionals and loops to control program flow.
5.4 Functions Organise code using reusable functions.
5.5 Arrays and Data Handling Manage collections of data using arrays.
6 Web Programming Concepts 6.1 DOM Manipulation Access and update web page elements dynamically.
6.2 Event Handling Handle user interactions like clicks and inputs.
6.3 Dynamic Content Create responsive and interactive web pages.
7 Animation and Game Development 7.1 Motion and Animation Create animations using movement and timing.
7.2 Game Logic Implement rules, scoring, and interactions.
8 AI in Web Applications 8.1 Pretrained Models Use ready-made AI models in applications.
8.2 Real-time Processing Work with live inputs such as webcam data.
8.3 Output Integration Convert AI outputs into actions like sound or alerts.
9 Multimodal AI Systems 9.1 Multi-Model Integration Combine multiple AI models in a single system.
9.2 Gesture-Based Interaction Build systems controlled by gestures and motion.
10 Personalised AI Systems 10.1 Transfer Learning Use existing models to build customised solutions.
10.2 Model Training Train simple AI models using custom data.
10.3 Model Evaluation Test and improve AI model performance.
11 Applied AI Projects 11.1 Problem Solving with AI Apply AI to solve real-world problems.
11.2 Project Development Design and build complete AI applications.
11.3 Testing and Debugging Improve solutions through testing and debugging.