Course code: 0741345

Course Title: Data modelling and Simulation

Course prerequisite (s) and/or corequisite (s): 0721224

Course Level: 3

Credit hours: 3

Data modelling and simulation deals with the statistical description of data, data-fitting methods, regression analysis, analysis of variance, and goodness of fit. Probability and random processes, discrete and continuous distributions, Central Limit Theorem, a measure of randomness, Monte Carlo methods. Stochastic Processes and Markov Chains, Time Series Models. Modelling and simulation concepts, Discrete-event simulation: Event scheduling/Time advance algorithms verification and validation of simulation models. Continuous Simulation: Modelling with and Simulation of Stochastic Differential Equations

Course code: 0741376

Course Title: Pattern Recognition                     

Course prerequisite (s) and/or corequisite (s):

0741371

Course Level: 3

Credit hours: 3

Pattern recognition covers the fundamentals of creating computational algorithms that recognize and analyse patterns within data of various forms. Topics and algorithms will include fractal geometry, classification methods such as random forests, deep learning recognition approaches, and human visual system models. Python and stat-of-the-art packages like Tensorflow which will be used as a mechanism for students to study patterns in nature, noise, and data from various real-world sources, such as images, social media, and biomedical signals.

Course code: 0741379

Course Title: Intelligent Mobile Robotics

Course prerequisite (s) and/or corequisite (s):

0741371

Course Level: 3

Credit hours: 3

Intelligent Mobile Robotics covers the fundamental elements of mobile robot systems from a computational standpoint. Issues such as software control architectures, sensor interpretation, map building, and navigation are covered, drawing from current research in the field. Students program a small mobile robot to perform simple tasks in real-world environments.

Course code: 0741474

Course Title: Natural language Processing (NLP)

Course prerequisite (s) and/or corequisite (s): 0741375

Course Level: 4

 Credit hours: 3

Natural language Processing (NLP) deals with the interaction of human languages with the computer. Specifically, how to program a computer to analyse and process a large amount of text. NLP covers the topics of Linguistics, formal Language Syntax and Structure, Natural Language Processing, programming language Syntax and Structure, Control flow, text Tokenization, Text Normalization, Understanding Text Syntax and Structure, Text Summarization and Information Extraction, Feature Matrix, Single Value Decomposition, Automated Document Summarization, Semantic Analysis.

Course code: 0741478

Course Title: Neural Networks

Course prerequisite (s) and/or corequisite (s):

0741372

Course Level: 4

Credit hours: 3

Synopses:

Basic Architecture of Neural Networks, Single Computational Layer: The Perceptron, Multilayer Neural Networks, Training a Neural Network with Backpropagation. Machine Learning with Shallow Neural Networks.

Course Description:

This course covers the Artificial Neural Network concept. Single-layer and multi-layer neural networks. Various learning rules; perceptron, delta, and backpropagation concept. The method of least squares as a basis for model building. The least-mean-square (LMS) algorithm. Self-organizing maps. Stochastic machines, statistical, neuro-dynamic concept.

Course code: 0741452

Course Title: Big Data

Course prerequisite (s) and/or corequisite (s):

0741375

Course Level: 4

 Credit hours: 3

Synopsis:

How storing  big data, Analyze big data, spark, Hadoop,

Course Description:

The course introduces high performance computing concepts to solve Big Data problems. Strength and limitations of Big Data usage are discussed in depth using real-world examples. Students then engage in case study exercises in which small groups of students develop and present a Big Data concept for a specific real-world case. This includes practical exercises to familiarize students with the Big Data tools and infrastructure. It also provides a first hands-on experience in handling and analyzing large, complex structured, semi-structured, and unstructured data. Upon successful completion of this course, students should be able to understand how and why to use Big Data tools to solve Big Data problems.

Course code: 0741375

Course Title:  Deep learning

Course prerequisite (s) and/or corequisite (s):

0741372

Course Level: 3

Synopsis:

Basic neural networks, (CNN) Convolutional Neural Network and (RNN) Recurrent Neural Network structures, Deep learning models are based on deep neural networks with large set of labeled data and back propagation and forward propagation techniques. These are capable of Supervised and Unsupervised Learning and reinforcement learning, and applications to problem domains like speech recognition and computer vision.

Course Description:

This course will introduce students to the concept of Deep learning and it will help students to understand its key principles. The course covers feed-forward neural networks, convolutional neural networks, recurrent neural network, sequence modelling, deep reinforcement learning, and other fundamental concepts and techniques. This course will also teach the students the key computations underlying deep learning. It is expected by the end of the course, students will be able to build, train and apply fully connected deep neural networks, and to know how to implement efficient neural networks using the most popular libraries for Deep Learning such as Keras, PyTorch; and Tensorflow. The course will introduce students also to a wide spectrum of deep learning applications in real-word problems.

Course code: 741351

Course Title: Computing systems for data science and artificial intelligence

Course prerequisite (s) and/or corequisite (s):

750335

Course Level: 3

 Credit hours: 3

The goal of this course is to provide students an overview of the various programs and equipment that help data scientists analyze their data. These technologies include R, Hadoop, Spark, and more. It also provides an introduction to big data, cloud, and Internet of Things computing.

Course code: 0741372

Course Title: Machine learning

Course prerequisite (s) and/or corequisite (s):

0741273

Course Level: 3

Credit hours: 3

Linear Regression, Linear Classification, Basis Expansions and Regularization, Decision Trees and Related Methods, Model Assessment and Selection, Neural Networks, Random Forests and Boosting, Support Vector Machines, Unsupervised Learning, Reinforcement Learning.

Course Description:

This course will give an undergraduate-level introduction to machine learning providing the foundations for mathematical models and algorithms required for machine learning tasks and their applications. Topics will include supervised learning, unsupervised learning, deep learning, and reinforcement learning. This course will put an emphasis on practical applications of machine learning in artificial intelligence such as computer vision, data mining, speech recognition, text processing and bioinformatics.

Course code: 741371

Course Title: Fundamentals of Artificial Intelligence

Course prerequisite (s) and/or corequisite (s): 741273

Course Level: 3

Credit hours: 3

This course introduces the basic principles, techniques, and applications of artificial intelligence. It also covers topics such as knowledge representation, logic, reasoning and problem solving, search algorithms, game theory, cognition, learning and planning methods, knowledge representation, computational logic, knowledge engineering and expert systems, and natural language processing. Machine learning and some important programming languages such as Python and R will be introduced.

Course code: 741444

Course Title:  Data Visualization

Course prerequisite (s) and/or corequisite (s): 741273

Course Level: 4

Credit hours: 3

This course introduces how to design and create data graphs based on the available data and the tasks to be achieved. Topics include data modelling, data manipulation, data exploration, linking data properties to graph properties, and dashboard development. Emphasis is placed on identifying patterns, trends, and differences in data across categories, space, and time. The student will learn to evaluate the effectiveness of representative graphics, and to think critically about each design decision, such as choosing colour and choosing visual coding

Course code: 0741242

Course Title: Fundamentals of Data Engineering and analytics

Course prerequisite (s) and/or corequisite (s):

741141

Course Level: 2

Credit hours: 3

This course introduces the basic concepts of data science, analysis, and its applications. Topics covered in the course include data acquisition, cleaning and aggregation, data exploration, analysis and presentation, model building, analysis and verification, and statistical and mathematical foundations of data science. This course also deals with the data life cycle in a data science project, which covers data types, such as the structured, semi-structured and unstructured structure, the different data formats and techniques used, as well as exploration using basic data visualization or review techniques.

Course code: 741491

Course Title: Practical Training

Course prerequisite (s) and/or corequisite (s):

Exceeding 90 credit hours + Department Agreement

Course Level: 4

Credit hours: 3

Practical Training: The student must train in an institution or company related to information technology, data and artificial intelligence for a period of no less than eight weeks and pass at least (15) hours of practical training per week. During the training, the student is required to perform some tasks related to his specialization, such as developing a program or learning new skills, techniques, and abilities. Students are followed up by designated supervisors to assess performance.

Course code: 741492

Course Title: Research Project (1)

Course prerequisite (s) and/or corequisite (s):

Exceeding 90 credit hours + Department Agreement

Course Level: 4

Credit hours: 1

The graduation project aims to develop the student's skills and ability to solve real-world problems, study and analyze them, and develop the necessary software to solve them. This is achieved by employing the knowledge gained from the courses they have studied in the implementation of this project. Under the supervision of a faculty member. The success of a project is determined to a large extent by whether the team has adequately resolved a customer problem. This project is evaluated by a panel of faculty members in the college.

Course code: 741493

Course Title: Research Project (2)

Course prerequisite (s) and/or corequisite (s):

741492

Course Level: 4

Credit hours: 2

This course is the second and complementary part of Research Project 1 where the knowledge gained from the courses is applied throughout the programme. The project must be performed by a group of students under the supervision of a faculty member. Students required to develop a complete implementation that achieves project objectives and submit a final report. The project must be submitted to a committee of faculty members.

Course code: 741458

Course Title: Cloud Computing

Course prerequisite (s) and/or corequisite (s):

741375

Course Level: 4

Credit hours: 3

This course aims to provide an introduction to cloud computing, its technologies, and the main components. It covers the topics of infrastructure, platform and applications, virtualization, cloud storage, and programming models. It discusses motivating factors, benefits, challenges, and Service forms. This course covers the following topics: cloud computing fundamentals, terminology and concepts, virtualization, deployment models, and service models, cloud technology enablement, and security assessment for cloud computing.

Course code: 0740141

Course Title: Fundamentals of Data Science

Course prerequisite (s) and/or corequisite (s):

0750110

Course Level: 1

Credit hours: 3

This course aims to provide an introduction to Data Science, Data Collection and Data Pre-Processing , Exploratory Data Analytics; Descriptive Statistics, Model Development, Model Evaluation.

Course code: 0740243

Course Title:  Statistical Tools for Data Science

Course prerequisite (s) and/or corequisite (s):

0250231

Course Level: 2

Credit hours: 3

This course aims to learn data science tools in the field of data visualization; Tableau, data science tools for exploratory data analysis; RapidMiner, Power BI, Data Science Tools for Data Storage; Apache Hadoop, Data Science Tools for Data Modeling; Data Robot, WEKA, R, SAS, SPSS, KNIME, Python.

Course code: 0740273

Course Title: Data Sciences and Artificial Intelligence Programming

Course prerequisite (s) and/or corequisite (s):

0250223

Course Level: 2

Credit hours: 3

The objective of this course is to implement data science models and synthetic models using programming languages and/or data science and artificial intelligence tools such as; Python, SAS, and WEKA are in various stages of the model development process such as; data preprocessing, exploratory data analytics; Descriptive statistics, model development, and model evaluation.

Course code: 0740354

Course Title: Data Mining

Course prerequisite (s) and/or corequisite (s):

0750260

Course Level: 3

Credit hours: 3

The objective of this course is to define data mining, data mining applications, for example) banking, insurance, customer loyalty, credit card, data mining cycle, data mining methodology and building a data mining environment, data preparation, exploratory data analysis, data mining techniques, Including: regression, decision trees, artificial neural networks, cluster analysis.

Course code: 0741477

Course Title: Business intelligence

Course prerequisite (s) and/or corequisite (s):

0741371

Course Level: 3

Credit hours: 3

The objective of this course is to learn the basics of business intelligence; Introduction and overview of business intelligence for supply chain and marketing, business intelligence and big data from the business side, understanding OLAP, dashboard development, predictive analytics, descriptive analytics, business intelligence project creation, data mining, generation of data mining queries and reports.