1. Introduction & Foundations: What is Artificial Intelligence? History & evolution , Types of AI – Narrow, General, Super AI vs ML vs Deep Learning vs Data Science, Real-world applications of AI
2. Mathematical Basics for AI: Linear algebra essentials (vectors, matrices), Probability & statistics basics
Optimization & gradient descent
💻 3. Programming for AI: Python fundamentals for AI , Using libraries – NumPy, Pandas, Matplotlib, Data preprocessing & visualization
🤖 4. Machine Learning Core: Supervised vs unsupervised learning, Regression algorithms,
Classification algorithms, Clustering techniques, Model evaluation & validation
🧩 5. Deep Learning: Neural networks basics, Training deep models & backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN, LSTM)
🗣️ 6. Language & Vision: Natural Language Processing (NLP) basics, Transformers & Large Language Models, Computer Vision fundamentals
⚙️ 7. Building AI Systems: Feature engineering, Handling bias & overfitting, Hyperparameter tuning,
Model deployment basics
🧭 8. Responsible AI: AI ethics & fairness, Privacy & security, Explainable AI
🚀 9. Modern & Generative AI: Prompt engineering, Generative AI (text, image, audio),
Building chatbots & assistants
🛠️ 10. Practical Exposure: Working with APIs, Creating end-to-end AI projects, Capstone project
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Research Methodology Syllabus
1) Foundations of Research: Meaning, objectives, types of research (basic/applied; qualitative/quantitative), and how good questions are framed.
2) Literature Review & Problem Identification: Finding credible sources, synthesizing prior work, identifying gaps, and writing a clear problem statement with hypotheses or aims.
3) Research Design & Methods: Experimental vs survey designs, sampling techniques, variables, data collection tools, validity and reliability.
