10 Credits SPRING



Aims/Description: This module aims to teach students the theory and implementation of bio-inspired machine learning algorithms. Topics include: Supervised learning: the backpropagation algorithm. Learning and Memory in Brain Circuits and Artificial Neural Networks. Unsupervised Learning (e.g. Oja's rule/ Principal Component Analysis, Clustering/ Competitive Learning) Reinforcement Learning (e.g. Temporal Difference Learning: Q learning, SARSA,  and Deep Reinforcement Learning). As well as the material taught in class, students are expected to self-study relevant books and research articles and produce reports in research article styles.

Restrictions on availability: COMU101, COMU103, COMU06, COMU05, COMU117, COMU109, COMU118. Students from departments other than Computer Science will need to demonstrate an excellent understanding of programming (Python or Matlab) and mathematics. A level math is compulsory.

Staff Contact: VASILAKI ELENI
Teaching Methods: Lectures, Laboratory work, Independent Study
Assessment: Course work

Notes: This unit forms part of an accredited degree programme

Information on the department responsible for this unit (Computer Science):

Departmental Home Page
Teaching timetable

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NOTE
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

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Teaching methods and assessment displayed on this page are indicative for 2021-22. Students will be informed by the academic department of any changes made necessary by the ongoing pandemic.

Western Bank, Sheffield, S10 2TN, UK