In the field of machine learning, a strong understanding of the underlying mathematical principles is crucial for designing, optimizing, and interpreting machine learning models. Concepts such as linear algebra, probability theory, calculus, and optimization form the backbone of machine learning algorithms, enabling practitioners to make informed decisions about model selection, training, and evaluation. This module provides a comprehensive foundation in these mathematical principles, ensuring that students can grasp the theoretical underpinnings of machine learning techniques and apply them effectively in practice. Students will gain critical skills to: •Analyze and solve mathematical problems relevant to machine learning. •Develop a deep understanding of how mathematical concepts such as matrix operations, gradients, and probability distributions are applied to machine learning algorithms. •Understand and implement optimization techniques for model training. •Build intuition on how mathematical structures impact model performance and decision-making. •Equip themselves with the necessary tools to move from theoretical concepts to practical applications in real-world machine learning systems. As the demand for skilled machine learning professionals continues to grow, this module aims to provide students with the mathematical knowledge and analytical thinking required to excel in this rapidly evolving field. This module aims to enable students to: •Understand the key mathematical concepts that form the foundation of machine learning. •Apply mathematical principles to analyze and solve machine learning problems. •Develop and optimize machine learning models using rigorous mathematical techniques.
Students completing the module successfully should be able to: A. Demonstrate a deep understanding of the mathematical principles underlying key machine learning algorithms, including linear algebra, calculus, probability, and optimization techniques. B. Apply matrix operations, vector calculus, and gradient-based optimization methods to solve problems in machine learning model design and implementation. C. Analyze the role of probability theory and statistics in machine learning, specifically in model evaluation, uncertainty estimation, and decision-making. D. Evaluate and interpret the mathematical structure of machine learning models, assessing the impact of various parameters and hyperparameters on model performance. E. Develop the ability to implement optimization algorithms and mathematical tools for efficiently training and tuning machine learning models in practical applications.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. This module is delivered with a combination of delivery in lectures, laboratory exercise, tutorials and a seminar at the end of the delivery. The concepts introduced during the lecture are illustrated using step-by-step analysis of practical training, complete case studies and live programming tutorials. In the laboratory practice, students will have opportunities to solve a set of exercises during the laboratories under the supervision of the lecturer and the teaching assistant. At the end of each week, there will be a tutorial to emphasize keynotes that have been discussed in lectures and laboratory practice during that week. At the end of the delivery, there will be a seminar to review the whole module delivery.