The subject of artificial intelligence and machine learning draws attention at CIU

CIU Department of Electric-Electronic Engineering organized a seminar in relation to Artificial Intelligence and Machine Learning Promotion and Applications.

During the event, Assist. Prof. Dr. Ghazal Sheikhi and CIU academic staff Dr. Payam Zarbakhsh, provided information in relation to artificial intelligence and machine learning.

Sheikhi informed that artificial intelligence has not only obtained an important place in everyday life, but has additionally established an active academic field within the last ten years, while also advising that it is important for engineering students to learn what artificial intelligence can do, how it works, and how to use it efficiently within their areas of expertise.

Providing information in his speech in relation to the basic artificial intelligence and machine learning concepts, as well as providing information in relation to the deep learning technique with a series of examples, Assist. Prof. Dr. Sheikhi went on to say, “Machine Learning algorithms, and more specifically, deep learning technique, are based on complex mathematical models and multi-process calculations”.

Noting that the newly emerging tools such as Google Colab, PyTorch etc. allow students or engineers to easily apply machine learning algorithms, Sheikhi explained that everything within machine learning consists of mathematics, algorithms, matrixes, and multiplications.

Stating within his speech that four important machine learning components can be found, including data, model, loss function, and the optimization algorithm, Dr. Zarbakhsh went on to state, "Depending on the task at hand, the data format, amount of data, and the available hardware, the most appropriate model can be chosen".

Zarbakhsh explained that with the emergence of the graphics processing unit and Tensor processing unit, deep learning has achieved great success in the last ten years, and continued, “Deep learnings important disadvantage is in its potential failures. No matter how smart they may seem, these modes are not actually capable of mimicking human understanding".

Expressing that machine learning has a number of areas where it can be applied and used effectively, Zarbakhsh concluded, “In addition to frequently used applications such as web searches, image recognition, language translation, etc. There are a variety of possible areas that are based on deep learning, such as traditional machine learning algorithms, electrical engineering, biomedical engineering, industrial engineering, business, and many other fields”.