About This Course

The course Advanced Machine Learning and Artificial Intelligence (32-06-1705) is designed for advanced undergraduate and graduate-level students who aim to develop a deep, conceptual, and practical understanding of machine learning (ML) and artificial intelligence (AI). Through a blend of theoretical exposition, hands-on programming, and critical analysis, this course provides the foundations and frontiers of AI technologies in a wide range of real-world applications. 

Students will engage with core topics, including supervised and unsupervised learning, neural networks, deep learning, natural language processing, reinforcement learning, ensemble methods, explainable AI, and ethical AI design. Emerging trends, including generative models, federated learning, multimodal AI, and AutoML, will also be covered. Practical labs and case studies will ensure that students not only grasp abstract concepts but can apply them using state-of-the-art tools, including TensorFlow, PyTorch, Scikit-learn, Hugging Face, and Streamlit. 

The course adopts a comprehensive pedagogical framework, combining lectures, lab exercises, capstone projects, and real-world case studies from diverse domains, including healthcare, finance, the environment, and public safety. Upon successful completion, students will be equipped to design, implement, and evaluate AI systems responsibly and effectively, addressing modern challenges with scalable, interpretable, and ethical solutions. 

Requirements