Having been realized within the body of Near East University, the Course on Artificial Intelligence and Its Apps attracted intensive attention

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Added On: 14 May 2019, Tuesday, 10:58
Last Edited On: 14 May 2019, Tuesday, 10:58

Having been realized within the body of Near East University, the Course on Artificial Intelligence and Its Apps attracted intensive attention

Near East University Experimental Health Sciences Research Center (DESAM) organized a course on “Artificial Intelligence and its Applications”.

The Directorate of Press and Public Relations Office of Near East University released that the course participants were provided with basic information about the introduction and theoretical details of machine learning since artificial intelligence is the simulation of human intelligence processes by machines. Within the scope of the course the machine learning types were explained and examples on controlled learning were given. Besides, participants were provided with comprehensive information regarding linear regression, logistic regression, decision tree learning and support vector machines that are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

Topics such as Data Normalization, a technique often applied as part of data preparation for machine learning, Data Binning, a technique used to reduce the effects of minor observation errors, Standardization, a method used for rescaling data in machine learning, Missing data imputation and Confusion matrix were covered during the course. Another core topic of the course was the deep learning which uses neural networks to increase the computational work and provide accurate results. Applications regarding the use of deep learning in medicine and machine learning were carried out within the scope of the course.

Having been realized within the body of Near East University, the Course on Artificial Intelligence and Its Apps attracted intensive attention

The basics of deep learning, cross entropy and loss, activation functions, optimization of weights and prejudices with back propagation and gradient descent, how to create (deep) neural networks with Keras and TensorFlow, how to record and upload models and model weights, how to make prediction on test data were discussed via practical applications.

By using machine learning, the latest developments in the fifth generation (5G) wireless technology, the use of 5G as the next generation mobile internet connectivity in internet operating system (IOS), and the crucial role of the Internet of Things (IoT) in ensuring significant advancement in educational sciences such as medicine were addressed within the frame of the course covering advancements in technology.

During the course covering the preparation of digital images as the pre-treatment process for machine learning, the importance of data selection, data preprocessing and data transformation as well as the problems originated from missing or bad data inputs were addressed.

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