MLx Fundamentals

Building on the accomplishments of our previous programs, and with a commitment to ensuring that all participants, including those new to the theory and fundamentals of modern ML, have a strong foundation, this module is designed to provide comprehensive training in the following key areas:

• Linear Algebra and Mathematics of Machine Learning: A solid grasp of linear algebra is essential for understanding the mathematical underpinnings of ML.
• Optimisation: Explore the optimisation techniques crucial for fine-tuning machine learning models and achieving optimal results.
• Fundamentals of Statistical/Probabilistic ML: Gain a deep understanding of the statistical and probabilistic foundations that form the bedrock of modern ML.
• Fundamentals of Representation/Deep Learning: Delve into the core concepts of representation and deep learning, essential for grasping the intricacies of advanced ML techniques.

Note: All eligible candidates (with a relevant background in computer science/ML) are welcome to register for this event. (Unlike the other OxML courses, this module is not subject to a selection process).