Master of Science in Computer Science
Studying Computer Science
The Master of Science (MS) in Computer Science is an advanced graduate program designed to equip students with comprehensive knowledge and cutting-edge skills in the field of computer science. This program offers a blend of theoretical foundations and practical experience, covering a wide range of topics including algorithms, software engineering, data science, artificial intelligence, cybersecurity, and more. Through a combination of rigorous coursework, research opportunities, and hands-on projects, students will develop the expertise needed to solve complex computational problems and innovate in various domains. Whether students choose to specialize in a particular area or pursue a broad-based curriculum, the program emphasizes critical thinking, problemsolving, and the application of advanced technologies. Graduates will be well-prepared for leadership roles in academia, industry, and government, ready to drive technological advancements, and address the challenges of a rapidly evolving digital world.
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Artificial Intelligence Track
This track is designed for CS graduates seeking to deepen their expertise in the rapidly evolving field of AI. The program provides a rigorous and comprehensive curriculum that encompasses advanced theoretical foundations, cutting-edge research, and practical applications of AI technologies. Students will engage with topics such as machine learning, deep learning, natural language processing, reinforcement learning, and AI ethics. Through a combination of core courses, specialized electives, and a capstone project or thesis, graduates will be equipped with the skills and knowledge necessary to lead and innovate in academic, industrial, and governmental roles. Our program emphasizes hands-on experience, ethical considerations, and the application of AI in solving realworld problems, preparing students to make significant contributions to the field and society.
Credits
To Graduate
First Year 18 Credits
Fall Semester
This course provides an in-depth introduction to the field of machine learning, focusing on both theoretical foundations and practical applications. Students will learn various algorithms and techniques, including supervised learning, unsupervised learning, and reinforcement learning. The course will cover essential concepts such as data preprocessing, model training, evaluation, and optimization.              Topics include: Supervised Learning: Linear and logistic regression
Decision trees and random forests Support vector machines SVM, Neural networks and deep learning; Unsupervised Learning: Clustering algorithms (K-means, hierarchical clustering), Principal Component Analysis (PCA). Practical implementation of algorithms using programming languages such as Python and libraries like scikit-learn, TensorFlow, and PyTorch
This course provides a comprehensive examination of the ethical, policy, and privacy issues related to data collection, usage, and management. Students will explore the ethical implications of datadriven decision-making, learn about the regulatory landscape governing data privacy, and
understand the best practices for ensuring responsible data stewardship. This course is ideal for students and professionals interested in the ethical, legal, and societal implications of data and technology, aiming to promote responsible and informed data practices.in various domains.
This course introduces the fundamental concepts and techniques of computer vision, enabling students to understand and develop systems that can interpret and analyze visual information from the world. Topics include: image processing, feature extraction, object recognition, motion analysis, and deep learning for vision. It includes: Image Processing, filtering and edge detection, color processing and transformations; Object Detection and Recognition; Motion Analysis and optical flow and tracking; 3D Vision and Deep Learning for vision.
Spring Semester
Deep learning is a subset of machine learning that focuses on neural networks with many layers (hence “deep”). This course covers the
following topics: Introduction to Neural Networks; Basic concepts of neurons and neural networks; Activation functions (e.g., sigmoid, ReLU); Feedforward neural networks.; Training Neural Networks; Loss functions and optimization techniques (e.g., gradient descent, stochastic gradient descent); Backpropagation algorithm; Deep Learning Architectures; Convolutional Neural Networks (CNNs) for image processing; The course often includes hands-on projects and assignments to help students gain practical experience in building and deploying deep learning models and apply the of Deep Learning on Computer vision (e.g., image classification, object detection), natural language processing, speech recognition and synthesis.
This course provides an introduction to the field of Natural Language Processing (NLP), focusing on the computational techniques and models used to process and analyze human language. Students will learn about various NLP tasks, including text classification, sentiment analysis, machine translation, and language generation, and will explore both traditional approaches and modern deep learning techniques. Topics include: Text Preprocessing; Text Classification and Sentiment Analysis; Deep Learning for NLP; Machine Translation and Sequence-to Sequence Models; Language Generation
Biometrics is a field of study that involves the statistical analysis of biological data. This course covers the following topics: Introduction to
Biometrics; Biometric Systems Component; Biometric Modalities: Fingerprint recognition, face recognition, voice recognition, Pattern recognition and Matching; Performance Evaluation; Security and Privacy. This course is designed for students in computer science, information technology, and related fields who are interested in the rapidly evolving area of biometric technology and its applications.
Second Year 21 Credits
Fall Semester
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. This course covers the following topics:
Introduction to Reinforcement Learning; Basics of RL, Markov Decision Processes (MDPs);
Dynamic Programming; Monte Carlo Methods; Temporal Difference (TD) Learning;
Function Approximation; Policy Gradient Methods; Advanced Topics in Reinforcement Learning. Applications of Reinforcement Learning: Robotics, game playing and autonomous systems (e.g., self-driving cars). The course often includes theoretical lectures, practical assignments, and projects to provide students with both the theoretical understanding and practical skills necessary to develop and apply RL algorithms.
Spring Semester
Advanced topics in AI and data science dive deeper into sophisticated techniques and emerging trends in the field.
These topics often involve complex mathematical concepts and require a solid understanding of foundational AI and data science principles. Key areas include: Deep Learning; Reinforcement Learning; Generative Models; Bayesian Methods; Big Data Technologies; Optimization Techniques; Computer Vision.
Data Science Track
This track enables CS graduates to master the skills necessary to analyze and derive actionable insights from complex data sets. This program offers a comprehensive curriculum that blends theoretical foundations, advanced computational techniques, and practical applications in data science. Students will delve into topics such as machine learning, big data analytics, statistical modeling, data visualization, and data ethics. Through a combination of core courses, specialized electives, and a capstone project or thesis, graduates will be well-prepared to tackle data-driven challenges in various industries, including technology, healthcare, finance, and government. Our program emphasizes hands-on experience with the latest tools and technologies, fostering the ability to transform data into strategic assets. By the end of the program, students will possess the expertise to lead data science initiatives and drive innovation in their respective fields.
Credits
To Graduate
First Year 18 Credits
Fall Semester
This course provides an in-depth introduction to the field of machine learning, focusing on both theoretical foundations and practical applications. Students will learn various algorithms and techniques, including supervised learning, unsupervised learning, and reinforcement learning. The course will cover essential concepts such as data preprocessing, model training, evaluation, and optimization.              Topics include: Supervised Learning: Linear and logistic regression
Decision trees and random forests Support vector machines SVM, Neural networks and deep learning; Unsupervised Learning: Clustering algorithms (K-means, hierarchical clustering), Principal Component Analysis (PCA). Practical implementation of algorithms using programming languages such as Python and libraries like scikit-learn, TensorFlow, and PyTorch
This course provides a comprehensive examination of the ethical, policy, and privacy issues related to data collection, usage, and management. Students will explore the ethical implications of datadriven decision-making, learn about the regulatory landscape governing data privacy, and
understand the best practices for ensuring responsible data stewardship. This course is ideal for students and professionals interested in the ethical, legal, and societal implications of data and technology, aiming to promote responsible and informed data practices.in various domains.
This course covers the principles and practices of presenting data in a visual format to facilitate understanding and decision-making. This
course aims to develop skills in both the technical aspects of creating visualizations and the conceptual understanding of how to use them to convey information effectively. Key 192 components of the course include: Principles of Visualization; Types of Visualizations; Tools and Software: Hands-on training with popular visualization tools and software, such as Python libraries like Matplotlib and Seaborn; Data Preparation: Techniques for cleaning, transforming, and structuring data to ensure it is ready for visualization;
Interactive Visualization: Techniques for creating interactive and dynamic visualizations that allow users to explore data on their own; Case Studies and Applications: Real-world examples and projects to apply visualization techniques to various types of data and
domains.
Spring Semester
Time series analysis is a branch of statistics and data science that deals with analyzing and forecasting data points collected or recorded at specific time intervals. This course provides the necessary theoretical background and practical skills to analyze temporal data. Key Topics Covered:
Introduction to Time Series Analysis; Time series components: trend, seasonality, cycle, and irregular components; Time domain versus frequency domain analysis; Autocorrelation and partial autocorrelation functions; Time Series Decomposition: Additive and multiplicative models; Smoothing Methods: Moving averages and Exponential smoothing: Simple, Holt, and Holt-Winters methods; Time Series Model:
Autoregressive (AR) models, Moving Average (MA) models, Autoregressive Integrated Moving Average (ARIMA) models; Seasonal ARIMA (SARIMA) models; Vector Autoregression (VAR) models; Forecasting; Applications with Eviews software.
Advanced Inference Statistics delves into the theoretical foundations and practical applications of statistical inference methods. Key Topics include: Probability distribution: T (Student), X2(Pearson), and F (Fisher) distributions. The sampling theory, the central limit theorem. The estimation theory: confidence interval, estimation of the mean and variance from one sample, Estimation of the difference of means from two samples, Estimation of the ratio of variances from two samples, estimation of proportions, Bayesian estimation and Maximum likelihood estimation. Hypothesis test: The null and alternative hypothesis, level of significance, critical values, p-values, comparing the difference between 2 means, comparing several means, analysis of variance ANOVA .comparing the ratio of 2 variances. Nonparametric tests. Regressions and multiple regressions. Applications with R, Excel and SPSS.
This course introduces data mining as the process of discovering patterns, correlations, and anomalies in large datasets to support prediction
and decision-making. Topics include data pre-processing, integration, transformation, dimensionality reduction, discretization, and normalization. Students will explore classification methods such as decision trees, random forests, k-NN, Naive Bayes, logistic regression, and SVMs, along with model evaluation techniques like confusion matrices, ROC curves, and cross-validation. Clustering techniques, including k-means and hierarchical clustering, are also covered. Through lectures, hands-on exercises, and projects, students will gain theoretical and practical experience using tools like R and Python with libraries such as Scikit-learn, pandas, and TensorFlow.
Second Year 21 Credits
Fall Semester
Game theory is the study of mathematical models of strategic interaction among rational decision-makers. The course covers topics such as:
Basic Concepts: Definitions of games, strategies, payoffs, and equilibria; Types of Games: Zero-sum games, cooperative vs. non-cooperative games, and dynamic games.; Nash Equilibrium: The concept where no player can benefit from changing their strategy while others keep theirs unchanged; Mixed Strategies: Strategies that involve randomizing choices to achieve a better outcome; Repeated Games: Analysis of games played more than once, allowing for strategies that depend on previous outcomes; Bayesian Games: Games with incomplete information where players have beliefs about the types of other players; Applications: Real-world applications in economics, political science, and social sciences.
Spring Semester
Advanced topics in AI and data science dive deeper into sophisticated techniques and emerging trends in the field.
These topics often involve complex mathematical concepts and require a solid understanding of foundational AI and data science principles. Key areas include: Deep Learning; Reinforcement Learning; Generative Models; Bayesian Methods; Big Data Technologies; Optimization Techniques; Computer Vision.
Networking Track
This track is designed to equip students with advanced knowledge and practical skills in the design, implementation, and management of complex network infrastructures. This specialized track covers a broad range of topics, including network architecture, advanced networking protocols, network security, and performance optimization. Students will engage in hands-on learning experiences and cutting-edge research to address contemporary networking challenges. Through this track, graduates will be prepared to take on critical roles in ensuring the efficiency, reliability, and security of network systems across various industries, positioning themselves as leaders in the field of networking technology
Credits
To Graduate
First Year 18 Credits
Fall Semester
This course provides an in-depth exploration of advanced concepts in network design and architecture. Students will engage with both theoretical and practical aspects of modern network systems, focusing on the design principles and architectural frameworks that support
193 large-scale, high-performance networks. Topics include: Advanced routing and switching techniques, network protocols and their optimization, network security and resiliency, software-defined networking (SDN) and network function virtualization (NFV), cloud and
data center networking, wireless and mobile networking, and performance analysis and network simulation. Students will work on projects that involve designing and implementing network solutions for complex scenarios, utilizing state-of-the-art tools and methodologies. By the end of the course, students will be equipped with the skills to design robust, scalable, and efficient network architectures suitable for a variety of applications in industry and research.
This course delves into the aspects of network security, exploring the theoretical foundations and practical applications necessary to protect networked systems. Students will learn about the latest techniques and technologies used to secure networks against various threats and
vulnerabilities. Key topics include: Fundamentals of network security, cryptographic protocols and their applications, authentication and authorization mechanisms, network intrusion detection and prevention systems (IDS/IPS), firewalls and VPNs, secure network architectures, wireless security, incident response and forensic analysis, emerging threats and advanced persistent threats (APTs), and security policies,
standards, and compliance. Through a combination of lectures, hands-on labs, and projects, students will gain practical experience in implementing and managing security measures in networked environments.
This course explores the fundamental and advanced concepts of cloud computing and virtualization technologies. Students will gain a thorough understanding of cloud architecture, service models, deployment strategies, and the use of virtualization to optimize and manage computing
resources. Key topics include: Cloud service models: IaaS, PaaS, SaaS, cloud deployment models: public, private, hybrid, and multi-cloud, virtualization technologies, data centers and their design, cloud security and compliance, server less computing, and cloud migration and management. Students will engage in practical exercises using leading cloud platforms and virtualization tools, enabling them to design, deploy, and manage cloud-based solutions effectively. The course also covers current trends and future directions in cloud computing and virtualization.
Spring Semester
This course provides an in-depth exploration of advanced techniques and best practices in system and network administration. Students will learn to manage complex IT infrastructures, focusing on automation, security, performance optimization, and troubleshooting. Topics
include: Advanced server and network configuration, scripting for automation, network services management, security protocols, and incident response. Through hands-on labs and projects, students will gain practical experience in maintaining and securing largescale systems and networks, preparing them for senior roles in IT administration.
This course delves into the unique networking and security challenges posed by the Internet of Things (IOT). Students will learn about IOT architectures, communication protocols, and network design principles tailored for IOT environments. The course also covers critical security issues, including threat modeling, secure communication, authentication, and privacy in IOT systems.
Through hands-on projects and case studies, students will gain practical experience in 194 designing and securing IOT networks, preparing them for advanced roles in IOT development and cybersecurity.
This course provides a comprehensive overview of blockchain technology, exploring its underlying principles, architecture, and applications. Students will learn about distributed ledgers, consensus algorithms, smart contracts, and cryptographic techniques. The course covers
various blockchain platforms, use cases, and the potential impact of blockchain on industries such as finance, supply chain, and healthcare. Through hands-on labs and projects, students will gain practical experience in developing and deploying block chainbased solutions, preparing them for advanced roles in blockchain development and research.
Second Year 21 Credits
Fall Semester
This course explores the principles, technologies, and protocols underlying modern wireless networks. Students will study wireless communication fundamentals, including radio frequency (RF) principles, signal propagation, and modulation techniques. The course covers various wireless networking standards and technologies, such as Wi-Fi, Bluetooth, cellular networks, and emerging 5G networks. Security, performance optimization, and network management in wireless environments are also discussed. Through hands-on labs and projects, students will gain practical experience in designing, deploying, and troubleshooting wireless networks, preparing them for advanced roles in wireless communications and network management.
Spring Semester
This course examines cutting-edge developments and emerging trends in the field of Information Technology.
Students will explore a range of advanced topics such as artificial intelligence, machine learning, big data analytics, cybersecurity, cloud computing, and blockchain technologies. The course emphasizes the integration and application of these technologies to solve complex problems and drive innovation. Through lectures, case studies, and hands-on projects, students will gain insights into the latest IT advancements and their practical implications, preparing them for leadership roles in technology-driven organizations.
Cybersecurity Track
This track prepares students for the critical task of protecting information systems and networks from ever-evolving cyber threats. This specialized track delves into advanced topics such as cryptography, ethical hacking, digital forensics, and incident response. Students will gain hands-on experience and deep theoretical knowledge, enabling them to identify, analyze, and mitigate cybersecurity risks effectively. Through rigorous coursework and research, graduates will emerge as highly skilled professionals equipped to safeguard sensitive data and ensure the integrity and resilience of IT infrastructures across diverse industries. This track is ideal for those aiming to lead in the field of cybersecurity, driving innovation and strategic security initiatives.
Credits
To Graduate
First Year 18 Credits
Fall Semester
This course provides a comprehensive understanding of the methodologies and tools used in threat analysis and incident response. Students will learn to identify, analyze, and mitigate various cyber threats through the study of attack vectors, threat intelligence, and vulnerability
assessment. The course covers the development of incident response plans, forensic investigation techniques, and the use of security information and event management (SIEM) systems. Through hands-on labs and real-world scenarios, students will gain practical skills in detecting and responding to security incidents, preparing them for advanced roles in cybersecurity and incident management.
This course delves into the aspects of network security, exploring the theoretical foundations and practical applications necessary to protect networked systems. Students will learn about the latest techniques and technologies used to secure networks against various threats and
vulnerabilities. Key topics include: Fundamentals of network security, cryptographic protocols and their applications, authentication and authorization mechanisms, network intrusion detection and prevention systems (IDS/IPS), firewalls and VPNs, secure network architectures, wireless security, incident response and forensic analysis, emerging threats and advanced persistent threats (APTs), and security policies,
standards, and compliance. Through a combination of lectures, hands-on labs, and projects, students will gain practical experience in implementing and managing security measures in networked environments.
This course explores the principles and practices of risk management and compliance in the context of information technology and cybersecurity. Students will learn to identify, assess, and mitigate risks associated with IT systems and data, and understand the regulatory and compliance requirements relevant to various industries. Key topics include risk assessment frameworks, compliance standards (such as GDPR, HIPAA, and PCI-DSS), and the development of risk management strategies. Through case studies and practical exercises, students will gain skills in implementing effective risk management and compliance programs, preparing them for roles in risk management and regulatory compliance.
Spring Semester
This course examines cutting-edge developments and emerging trends in the field of Information Technology.
Students will explore a range of advanced topics such as artificial intelligence, machine learning, big data analytics, cybersecurity, cloud computing, and blockchain technologies.
The course emphasizes the integration and application of these technologies to solve complex problems and drive innovation. Through lectures, case studies, and hands-on projects, students will gain insights into the latest IT advancements and their practical implications, preparing them for leadership roles in technology-driven organizations.
Second Year 21 Credits
Fall Semester
This course focuses on the strategic and managerial aspects of cybersecurity, emphasizing leadership skills necessary for managing and directing cybersecurity teams and initiatives. Students will explore topics such as cybersecurity governance, risk management, incident response strategies, and the integration of security policies with business objectives. The course covers leadership principles, strategic planning, and the communication of security strategies to stakeholders. Through case studies, leadership exercises, and project work,
students will develop the skills to effectively lead cybersecurity efforts and manage complex security challenges, preparing them for senior roles in cybersecurity management.
Spring Semester
This course examines cutting-edge developments and emerging trends in the field of Information Technology.
Students will explore a range of advanced topics such as artificial intelligence, machine learning, big data analytics, cybersecurity, cloud computing, and blockchain technologies. The course emphasizes the integration and application of these technologies to solve complex problems and drive innovation. Through lectures, case studies, and hands-on projects, students will gain insights into the latest IT advancements and their practical implications, preparing them for leadership roles in technology-driven organizations.