Speciality Requirements Student must complete 27 credit hours

Course Code	Course Name	Credit Hours	Prerequests
467701	Introduction to Python Programming	3
This course is an introduction to programming with Python, one of the most popular and versatile programming languages used in a wide range of applications. Students will learn the basic concepts and syntax of Python, including control structures such as conditional statements, loops and functions, and explore important data structures such as lists, dictionaries, and sets. The course will cover the fundamentals of computer programming such as variables, data types, input/output, and debugging techniques. In addition, students will learn to design and implement basic algorithms in Python. The course will also introduce students to more advanced topics such as Object-Oriented Programming (OOP) concepts, exception handling, and regular expressions. The course is designed for beginners who have no prior programming experience, but it is also suitable for those who have some experience with programming in another language and want to learn Python. Through a combination of lectures, hands-on coding exercises, and assignments, students will gain practical experience in Python programming and develop a strong foundation in the language.
467702	Data Mining and Analysis Methods for Bioinformatics	3
This course will provide an introduction to data mining and analysis methods with applications from bioinformatics. Students will learn the principles of data mining, including data preprocessing, feature selection, clustering, classification, and association rule mining. They will also explore data analysis techniques such as statistical inference, hypothesis testing, and regression analysis. The course will cover a range of bioinformatics applications, including genomics, proteomics, and metabolomics. Students will learn how to apply data mining and analysis methods to biological data sets, including gene expression data, protein interaction networks, and sequence data. Throughout the course, students will gain hands-on experience with popular tools and software, including Python. They will also learn how to interpret and communicate results from data mining and analysis studies in the context of biological research. By the end of the course, students will have a solid foundation in the principles and techniques of data mining and analysis for bioinformatics. They will be able to design and execute their own data mining and analysis studies on biological data sets, and will be able to critically evaluate and interpret results from published studies in the field.
467703	Linear Algebra for Data Science	3
This course is designed to present and discuss those aspects of linear algebra that are most important in data analytics. The emphasis will be on developing intuition and understanding how to use linear algebra, rather than on proofs. The main topics include: Basic matrix operations, linear transformations Subspaces, ranges and null spaces, linear combinations and spans, linear independence, bases, dimension, rank and nullity theorem Systems of linear equations, symmetric matrices, inverses, determinants, triangular matrices, trace, eigenvalues and eigenvectors Positive definite matrices, covariance matrices, minimization problems involving matrices, minimization and convex functions. Orthogonal projections, Gram-Schmidt procedure, singular value decomposition Tensor structures and tensor trains
467704	Scientific Research	3
The course is designed to equip students with the fundamental skills necessary for conducting research in their field of study. The course covers techniques required for planning, designing, and executing a research project. Students learn how to identify research questions, develop hypotheses, and select appropriate data collection, analysis and evaluation methods. In addition, the course aims to develop students' skills in effective communication, including presenting research findings and writing research reports. Students will also have the opportunity to work collaboratively in teams to conduct research projects, developing skills in teamwork and project management.
467705	Machine Learning	3
This course provides an introduction to the fundamental concepts and techniques in machine learning. The course is designed for students with a basic background in mathematics and programming. The course will begin with an overview of the main types of machine learning problems and their applications. Students will learn about the differences between supervised and unsupervised learning, and how these techniques are used in real-world applications. The course will cover the key components of a machine learning system, including the data, the model, and the evaluation metrics. Students will learn how to acquire and preprocess data, and how to select appropriate models for different types of problems.
467706	Medical Image Analysis	3
This course provides an introduction to the field of medical imaging. It covers the fundamental principles of image acquisition, image processing, and analysis techniques, including enhancement, segmentation, feature extraction, and classification. The course also covers practical applications of medical image analysis, such as medical diagnosis, treatment planning, and research.
467707	Natural Language Processing with Applications on Healthcare	3
This course provides an introduction to fundamental topics on natural language processing, where students study the interaction between the computer system and natural languages. In this course, students learn how to build computer solutions for health-related tasks where natural (human) language is the main input. By the end of the course, students will have adequate knowledge, skills, and technologies that enable them to properly handle large amounts of textual content and extract useful information.
467799	Thesis	6
The Master Thesis course provides students with an opportunity to engage in original research, under the supervision of a faculty member, and produce a substantial piece of scholarly work that demonstrates their ability to independently carry out research, analyze data, and contribute to the knowledge base of their field of study. The course is designed to prepare students for doctoral study or professional practice in their field.

Speciality Optional Requirements Student must complete 9 credit hours

Course Code	Course Name	Credit Hours	Prerequests
467750	Artificial Intelligence: A Modern Approach	3
The main objective of this course is to provide students with an understanding of some basic methods and algorithms in artificial intelligence, as well as an appreciation for how to apply them to interesting practical problems through a number of examples. This course consists of three components: lectures, tutorials, and projects. The lectures will cover selected core topics such as search, game playing, decision making, and machine learning. The tutorial sessions will allow students to apply the algorithms to simple examples from "games." The projects will provide students with an opportunity to develop a small solution in various areas of artificial intelligence.
467751	Deep Learning	3
It is an advanced course focusing on the theory and practical applications of deep neural networks. Topics include various neural network architectures such as FNN, CNNs, and RNNs, optimization algorithms (SGD, Adam, etc.), regularization techniques (dropout, L2 regularization), and advanced topics such as generative adversarial networks (GANs). Students will implement deep learning models using frameworks like TensorFlow and PyTorch and explore cutting-edge research in the field.
467752	Intelligent Systems Management	3
This course aims to equip students with the necessary skills to manage various smart projects, such as planning skills, analytical abilities, risk management, communication and interpersonal skills, leadership skills in such systems, and time management. The course also includes the study of successful and unsuccessful models of smart projects.
467753	Advanced Data Structures and Algorithms	3
Appropriate Data Structures for Efficient Applications of Algorithms, Big Data Representation, NP and NP completeness, Introduction to Parallel Algorithms and Speculative Algorithms.
467754	Smart Systems Applications	3
The course covers the study of practical applications of smart systems in the real world. The student examines several practical applications through analysis and understanding of each field presented in the course. Additionally, they study the smart applications in each field and how these applications have contributed to improving the conditions and environment of each field, positively impacting them. This is done through studying and analyzing each system.
467755	Big Data in Medicine	3
This course provides an introduction to fundamental topics on natural language processing, where students study the interaction between the computer system and natural languages. In this course, students learn how to build computer solutions for health-related tasks where natural (human) language is the main input. By the end of the course, students will have adequate knowledge, skills, and technologies that enable them to properly handle large amounts of textual content and extract useful information.
467757	Knowledge Representation	3
This course is intended to provide students with enough information related to knowledge representation and analysis that contains subjects related to represent knowledge in several ways: Object Oriented, Structured Descriptions, Ontologies and their related issues such as Semantic Web, OWL, Ontologies comparisons, Similarity Measures. The course will also deal with studying some real problems and apply different knowledge representations and analysis on them.
467758	Internet of Things in Medicine	3
The course is designed to provide an overview of the principles and applications of the Internet of Things (IoT) in healthcare. It will cover topics such as IoT sensors, data acquisition and analysis, cloud computing, data security and privacy, and healthcare analytics. The course will also include case studies and practical exercises to develop skills in designing, implementing, and evaluating IoT solutions in healthcare.