1. Introduce some basic concepts and theories in linear algebra for machine learning 2. Introduce basic ideas and theories in the field of optimization for machine learning 3. Learn how to use mathematical tools such as linear algebra and optimization to solve problems in machine learning
A Demonstrate a foundational understanding of key concepts and theories in linear algebra B Demonstrate the ability to apply linear algebra concepts effectively to address and solve problems in machine learning. C Understand the basic concepts and theories of optimization theory D Demonstrate the ability to apply knowledge of optimization theory to solve machine learning problems
In each normal week, students will be expected to attend a three-hour formal lecture and to participate in a one-hour supervised problem class. Lectures will introduce students to the academic content and practical skills which are the subject of the module, while problem classes will allow students to practice those skills. In addition, students will be expected to devote 8 hours of unsupervised time for private study. Private study will provide time for reflection and consideration of lecture material and background reading. Two assessments will be used to test to what extent practical skills have been learnt. A written examination at the end of the module will assess the academic achievement of students.