Data Modeling and Simulation

Data modeling and simulation deals with statistical description of data, data fitting methods, regression analysis, variance analysis, and goodness of fit. Probability and stochastic processes, discrete and continuous distributions, central limit theorem, randomness measure, Monte Carlo methods. Stochastic processes and Markov chains, time series models. Modeling and simulation concepts, discrete event simulation: event scheduling/advance time algorithms verification and validation of simulation models. Continuous simulation: modeling using stochastic differential equations and their simulation.

Pattern Recognition

Pattern recognition course focuses on learning the basics of creating computational algorithms to recognize and analyze patterns within data of different forms. This course includes algorithms, fractal geometry, classification methods such as random forests, recognition approach using deep learning, and models of the human visual system. Python packages and state-of-the-art tools such as TensorFlow and software tools will be used as a mechanism to study patterns in natural and noisy data from various real-world sources, such as images, social media, and biomedical signals.

Intelligent Mobile Robotics

Intelligent mobile robotics course covers the essential elements of mobile robot systems from a computational perspective, where issues such as software control structures, sensor interpretation, map building, navigation, and search in defined environments are covered. Students program a small mobile robot to perform simple tasks in real-world environments.

Natural Language Processing

Natural Language Processing (NLP) course focuses on the interaction of human languages with computers. Specifically, how to program computers to analyze and process large amounts of text. The course covers topics in linguistics, language grammar and formal structure, natural language processing, sentence construction and structure in programming languages, control flow, text encoding, text normalization, understanding text syntax and structure, text summarization and information extraction, feature matrix, singular value analysis, automatic document summarization, and semantic analysis.

Machine Learning

The objective of this course is to provide students with a university-level introduction to machine learning that provides the foundations of 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 focus on practical applications of machine learning in artificial intelligence such as computer vision, data mining, speech recognition, text processing, and bioinformatics.

Neural Networks

This course covers the concept of artificial neural networks. Single-layer and multi-layer neural networks. Different learning rules; perceptron concept, delta, and backpropagation. Least squares method as a basis for model building. Least Mean Square (LMS) algorithm. Self-organizing maps. Stochastic machines, statistical dynamic concept.

Deep Learning

This course will introduce students to the concept of deep learning and help students understand its fundamental principles. The class covers feedforward neural networks, Convolutional Neural Networks, Recurrent Neural Networks, sequence modeling, deep reinforcement learning, and other essential concepts and techniques. This course will also teach students the fundamental computations underlying deep learning. By the end of the course, students are expected to be able to build, train, and apply fully connected deep neural networks, know how to implement efficient neural networks using the most popular deep learning libraries such as Keras, PyTorch, and TensorFlow. This course will also introduce students to a wide range of deep learning applications in real-world problems.

Big Data

The course introduces high-performance computing concepts for solving big data problems. The power and limitations of using big data are discussed in depth using real-world examples. Students then engage in case study exercises where small groups of students develop and present a big data concept for a specific real-world case. This includes practical exercises to introduce students to big data tools and infrastructure. It also provides first hands-on experience in processing 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.

Computing Systems for Data Science and Artificial Intelligence

The objective of this course is to provide students with an overview of various software and hardware that help data scientists analyze their data. These technologies include R, Hadoop, Spark, and more. It also provides an introduction to big data, cloud computing, and IoT computing.

Fundamentals of Artificial Intelligence

This course introduces the fundamental principles, techniques, and applications of artificial intelligence. It covers topics such as knowledge representation, logic, reasoning and problem solving, search algorithms, game theory, perception and learning, 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.

Data Visualization

This course introduces how to design and create graphical data representations based on available data and required tasks. Topics include data modeling, data processing, data exploration, mapping data properties to visualization properties, and developing data dashboards. The focus is on identifying patterns, trends, and differences in data across categories, space, and time. Students will learn to evaluate the effectiveness of visualizations and think critically about each design decision, such as color selection and visual encoding choices.

Fundamentals of Data Engineering and Analytics

This course introduces the fundamental concepts of data science, its analysis, and applications. Topics introduced include data acquisition, cleaning, aggregation, data exploration and analysis and presentation, model building, analysis and validation, statistical foundations, and mathematical foundations of data science. This course also addresses the data lifecycle in a data science project covering data types such as structured, semi-structured, and unstructured data, different data formats and techniques used, as well as exploration using basic data visualization or display techniques.

Practical Training

Practical Training: The student must train in an institution or company related to information technology, data, and artificial intelligence fields for no less than eight weeks and complete at least 15 hours of practical training per week. During the training, the student is required to perform tasks related to their specialization, such as software development or learning new skills, techniques, and capabilities. Students are monitored by designated supervisors to evaluate performance.

Research Project 1

The graduation project aims to develop students' skills and ability to solve, study, analyze, and develop software solutions for real-world problems. This is achieved through employing knowledge acquired from courses studied in implementing this project, under the supervision of a faculty member. Project success is largely determined by whether the team has successfully solved the client's problem appropriately. This project is evaluated by a committee of faculty members in the college.

Research Project 2

This course is the second part and completion of Research Project 1, where knowledge acquired from courses throughout the program is employed. The project must be performed by a group of students under the supervision of a faculty member. Students are required to develop a complete implementation that achieves project objectives and submit the final report. The project must be presented to a committee of faculty members.

Cloud Computing

This course aims to provide an introduction to cloud computing, its technologies, and key components. It covers infrastructure, platform and application topics, virtualization, cloud storage, and programming models. It discusses motivating factors, benefits, challenges, and service models. This course includes the following topics: cloud computing fundamentals, terminology and concepts, virtualization, deployment models, service models, cloud enabling technologies, and security evaluation for cloud computing.

Fundamentals of Data Science

This course aims to provide students with various aspects of data science including introduction to data science, data collection, data preprocessing, exploratory data analytics, descriptive statistics, model development, and model evaluation.

Statistical Tools for Data Science

This course aims to learn data science tools in the field of data visualization such as Tableau; data science tools for exploratory data analysis such as RapidMiner, POWER BI; data science tools for data storage such as Apache Hadoop; data science tools for data modeling such as DataRobot, WEKA, R, SAS, SPSS, KNIME, Python.

Data Science and Artificial Intelligence Programming

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

Data Mining

The objective of this course is to introduce data mining definition, data mining applications (e.g., banking, insurance, customer loyalty, credit cards), data mining lifecycle, data mining methodology and building a data mining environment, data preparation, exploratory data analysis, data mining techniques including regression, decision trees, artificial neural networks, and cluster analysis.

Business Intelligence

The objective of this course is to understand business intelligence fundamentals; 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, creating business intelligence projects, data mining, and creating data mining queries and reports.