1.    Introduction to Python Programming
2.    Basics of Python 
3.    Data Structures in Python
4.    Scientific computation with Python and
5.    Python for Deep Learning

1.    Foundations of Machine Learning – The Machine Learning Paradigm
2.    Linear and Polynomial Regression
3.    K-Nearest Neighbors
4.    Linear Classification – Logistic Regression
5.    Bias Variance tradeoff, Regularization
6.    Evaluation methods

1.    Recap of Linear and Logistic Regression
2.    Multiclass Classification
3.    Artificial Neural Networks
4.    Optimization in Neural Networks
5.    Basics of Hyper parameter optimization
6.    Convolutional Neural Networks (CNN)
7.    Sequence Analysis Models

1.    Introduction to Generative Models and their role in Modern AI
2.    Generative Adversarial Networks (GANs) 
3.    Diffusion Models for image generation
4.    Transformer Architectures
5.    Large Language Models (such as ChatGPT)
6.    Applications of existing Generative AI models
7.    Future trends in Generative AI