Search results: 3125
This course focuses on learning data science through the interest to development and improvement of capability of solving rich problems from data point of view in a systematic and principled way by using high quality instructions and basic level data science techniques. Students will be introduced to what data science is, will discover the applicability of data science across fields, and will learn how data analysis can help them make data driven decisions. Students will gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. This course provides the students with the required structure and responsibilities in order to educate them as data scientists progressing a right way with high concluding capabilities.
- Teacher: Kian Jazayeri
Data mining, which is the study of algorithms and computational paradigms that enable computers to search datasets for patterns and regularities and make predictions and forecasts is covered in this course. Knowledge discovery is introduced comprehensively. The course explores data selection, cleaning, coding, the application of various statistical and machine learning approaches, and visualization of the resulting structures, which are all steps in knowledge discovery. Students who successfully complete this course are supposed to learn about several data mining techniques, including classification, rule-based learning, decision trees, and association rules. Additionally, students are expected to learn about selection and cleaning of data, machine learning methods for "learning" about "hidden" patterns in data, and reporting and visualizing the resulting knowledge.
- Teacher: Kian Jazayeri
This course covers the algorithmic techniques and approaches required to handle various types of structured, semi-structured and unstructured data. The goal of the course is to teach algorithmic methods that serve as the cornerstones for handling and analyzing large datasets in a variety of formats. The course specifically covers how to pre-process big datasets, store big datasets effectively, design quick algorithms for big datasets, and evaluate the performance of designed algorithms. Algorithms for sorting, searching and matching as well as graph and streaming algorithms will be introduced. Upon completion of this course, students will have a broad knowledge of different algorithms for pre-processing, organizing, manipulating and storing different data types. Students will also be able to carry out performance analysis of each algorithm.
- Teacher: Emre Er
- Teacher: Zehra Ulucanlar
- Teacher: Soroush Ebrahimfar
- Teacher: Emre Er
- Teacher: Deniz GUrsoy
- Teacher: Abdallah Matari
- Teacher: Zehra Ulucanlar
- Teacher: Seda Yildirim
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- Teacher: Ayman Aboutaha
- Teacher: Begum Berkmen
- Teacher: Soroush Ebrahimfar
- Teacher: Ozgur Irmak
- Teacher: Abdallah Matari
- Teacher: Celil DInCer
- Teacher: Deniz GUrsoy
- Teacher: Ozgur Irmak
| To learn the students how to take a medical history, perform an extra-oral and intra-oral examination, have knowledge of radiation, radiation damages and radiation protection, evaluate the systemic symptoms, elicit the vital symptoms and the reason of the pain, elicit the maxilla, sinus, salivary gland, arthrosis pathology, and radiology, perform laboratory inspections and make a diagnosis and the treatment plan of the patients, perform an implant implementation and a radiographic assessment, elicit the lesions on radio-opaque, radiolucent and soft issue on bones. | |
Types of radiographs used in the maxillofacial region, pathologies in the jaws and their radiographic appearances
The contents of this course are the appliance construction techniques and mechanism of orthodontic tooth movements. Student recognizes removable orthodontic appliances used in the treatment of dental malocclusions, explains the purpose of the appliances. Learns how to bend adams clasp, springs and vestibular arch. Learns the appliance construction techniques. Describes the deciduous, mixed and permanent dentitions features, differentiates the normal and abnormal situations in teeth and dentition development with growth. Explains the mechanism of orthodontic tooth movement. Learns the response of tooth to applied orthodontic force. Explains the difference between physiological and orthodontic tooth movement. Learns the diagnostic modalities, important factors in severity of tooth resorption that can arise from orthodontic treatment and mostly affected teeth. Explains the local and general factors of malocclusion.
The course aims to provide essential information about the basic pathology of the diseases. It also aims to give basic information about pathology in dentistry.
- Teacher: Zehra Edebal