This module explores machine learning beyond supervised methods, focusing on unsupervised learning techniques like clustering and dimensionality reduction for uncovering patterns in complex data. Students learn explainability for transparent models, and refine academic writing, equipping them with technical and communication skills for advanced data science.

This module explores deep learning and its AI applications, covering neural networks, CNNs, and RNNs for tasks like image and text analysis. Students gain hands-on experience in NLP and computer vision techniques and participate in a Kaggle competition. Combining theory and projects, this module prepares students for real-world AI challenges.

This module explores Generative AI and Large Language Models (LLMs), focusing on Hugging Face, transformer architecture, and model training. Students learn LLM capabilities, including text generation, summarization, and question answering. They also study Retrieval-Augmented Generation (RAG) and gain hands-on experience fine-tuning models, preparing to apply generative AI to real-world challenges.

This module integrates leadership, ethics, and the broader context of your work. Students explore transformation, knowledge, and change, focusing on their project inquiries. They consider the nature of change they wish to promote, the ethical implications of their actions, and how these align with their leadership ambitions and future goals.