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:

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• Linear Algebra and Mathematics of Machine Learning: A solid grasp of linear algebra is essential for understanding the mathematical underpinnings of ML.

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• Optimisation: Explore the optimisation techniques crucial for fine-tuning machine learning models and achieving optimal results.

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• Fundamentals of Statistical/Probabilistic ML: Gain a deep understanding of the statistical and probabilistic foundations that form the bedrock of modern ML.

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• Fundamentals of Representation/Deep Learning: Delve into the core concepts of representation and deep learning, essential for grasping the intricacies of advanced ML techniques.