Bilgisayar Mühendisliği Bölümü Öğrencilerine Yönelik Yoğunlaşma Alanları

Bilgisayar mühendisliği lisans öğrencileri, lisans programlarının son iki yılında alacakları seçmeli derslerle aşağıdaki yoğunlaşma alanlarını tamamlayabilir, bu alanlarda mezuniyet sonrası iş bulabilir veya ilgili lisansüstü programlara devam edebilirler.

Yapay Zeka Yoğunlaşma Alanı - 5 ders (15 kredi)

Ders KoduDers AdıKrediAKTS
CENG 480Makina Öğrenmesi3 0 35
Overview of the learning process in humans and machines, machine learning concepts, algorithms and techniques. Topics includes features and patterns, distance metrics, classification, clustering, supervised and unsupervised learning, re-enforcement learning, decision trees.
CENG 464Veri Madenciliği3 0 35
Overview of data mining, exploring the data mining concepts, algorithms and techniques. Topics includes data mining process, pre-processing methods, classification algorithms (decision trees, rule-based, kNN algorithm), clustering algorithms (k-means, hierarchical clustering ), association rule mining (Apriori, FPGrowth), outlier detection.
CENG 481Yapay Sinir Ağları3 0 35
Overview of the artificial neural networks, the training algorithms, backpropagation, feedforward and recurrent networks. The main concepts in designing neural networks, and their main application areas are introduced as well.
CENG 474Veri Bilimi3 0 35
Introduction to data science by using tools and writing programs for acquiring, cleaning, analyzing, exploring, and visualizing data; making data-driven inferences and decisions; and effectively communicating results. Learning data manipulation, data analysis with statistics and machine learning, data communication with information visualization, working with data using recent techniques.
CENG 483Nesnelerin İnterneti3 0 35
This course is designed to provide students with knowledge and skills to build Internet of Things (IoT) systems. A systems engineering approach is adopted throughout the course reviewing the key technologies employed at different levels of the IoT stack and how they are integrated to form complete IoT systems. A number of devices, platforms and software tools will be introduced during the course from different vendors.
veya CENG 460Çokluortam Hesaplamaya Giriş3 0 35
Since multimedia data are often of bulky size, they have to be effectively compressed to be stored on storage media and transmitted over bandwidth-limited networks. A large number of multimedia coding algorithms have been invented to fulfill this challenge of data compression, and many multimedia coding standards and specifications have created by international standardization bodies and/or industrial consortiums. While multimedia coding defines representations of diverse types of digital information such as audio, speech, image and video, the course mainly concentrates on managing and processing these digital data. Therefore, it becomes more and more important to have an in-depth understanding on how multimedia coding works, not only for developers of multimedia coding systems, but also for users of these systems.
veya CENG 462Sayısal Görüntü İşleme2 2 35
Overview of Image model sampling and quantization, basic relationships between pixels and image geometry, two-dimensional Fourier transforms, image enhancement, spatial and frequency domain methods, image restoration, image segmentation.