COMP41390 Connectionist Computing

Academic Year 2023/2024

There are two distinct parts to this unit. In the first few lectures I will provide the students with a general overview of connectionism: its origins as an attempt to model the functioning of the brain, and the various classes of algorithms created starting from these foundations. In the second part I will zoom in on the last 10-15 years. I will focus on a general framework for designing machine learning models that deal with complex structured data. I will introduce graphical models and bayesian networks and describe inference and learning algorithms for them.In machine learning there are many details one doesn't want to talk about in public. During the class I will try to address some of these issues for the case of neural networks, i.e. to describe possible strategies for effectively training them in real-world scenarios.Throughout the class and especially towards the end I will show applications of connectionist models to real problems. Together with more classical fields such as image classification and language processing, I will spend some time on applications to biological data, which has been the main focus of my research for the last few years.

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Curricular information is subject to change

Learning Outcomes:

By the end of the unit I expect that the students will be knowledgeable enough to understand and decode most real-world connectionist systems by reading about them in technical articles and books.I also expect that the students will be able to design and implement a number of classes of connectionist tools, if needed in their future projects.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning








Approaches to Teaching and Learning:
Individual reading.
Individual task-based learning. 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Connectionist Computing (COMP30230)

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Summary of landmark paper 3 Unspecified n/a Graded No


Assignment: Summary of landmark paper 1 Unspecified n/a Graded No


Project: Implementation of connectionist tool Unspecified n/a Graded No


Assignment: Summary of landmark paper 2 Unspecified n/a Graded No


Examination: 2hr final exam 2 hour End of Trimester Exam No Graded No


Carry forward of passed components
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Lecture Offering 1 Week(s) - Autumn: All Weeks Mon 10:00 - 10:50
Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7 Wed 10:00 - 10:50
Lecture Offering 1 Week(s) - 8, 9, 10, 11, 12 Wed 10:00 - 10:50