GCTU has organised a machine learning and neural network practical exercise for Level 300 Computer Engineering students on Thursday, 20 August 2026, at the main Campus in Accra.
The exercise brought together about 170 students who participated in the one-hour session without using any form of Artificial Intelligence (AI) to create a machine learning model for the problem data set.
A computer engineering lecturer, Dr Isaac Osei Nyantakyi, who supervised the exercise, said this was the first time the students of the University had been introduced to the exercise and that henceforth, level 300 students would undertake it.
He stated that the exercise offers a taste of real-world AI environments and guides students through numerous computational problems that help them identify and build models from scratch.
He was confident that this would enable students to experience practical problem-solving, understand how data sets are created, how data sets are used in modelling machine learning algorithms and how these can be applicable, particularly in embedded systems, given their background in computer engineering.
“This particular exercise gives students a taste of the real-world environment where they are provided with a data set they have never seen before. It is an environment where they must think on their feet without any AI or external assistance.
“This is where they can apply everything they have learned in class in a real-world scenario. So, this truly helps build the confidence needed in the industry and research areas,” Dr Nyantakyi said.
A Level 300 student, Mr. Thomas Wewobunane, said the exercise was helpful, emphasising that he was able to learn a lot from it and would continue practising during his lecture hours.
He mentioned that it had helped him identify areas for improvement and thanked the University management for introducing them to this exercise.
Whereas machine learning – a part of artificial intelligence – enables students to learn from data and improve over time without writing new rules, a neural network is a specific type of machine learning model built like a human brain. Most users agree that machine learning is the broad field, while neural networks are one specific tool within that field.
