Earn a Master’s in Artificial Intelligence & Machine Learning Online

This graduate course provides a detailed overview of fundamental programming, design and testing concepts. Students are introduced to programming constructs and learn how to plan and create basic programming applications. Students will develop applications using common programming structures, which include: conditional statements, switches, loops, iteration control structures, and arrays. 

Credits: 3

In this course, students will be prepared to analyze organizational issues surrounding programming, network and software development, and propose the necessary solutions to address business needs. Students gain a detailed understanding of how to manage, oversee, plan and maintain technical personnel and resources. Students will also learn how to effectively relay technical information to all stakeholders in an organization. Topics include management principles for programmers and development specialists, technical communications, organizational leadership, risk management, project management, and systems maintenance. Students will prepare for cross-functional positions in team and industry settings in this course. 

Credits: 3

In this course, students will learn about the ethical considerations and issues programmers and software developers encounter in the workplace related to data, electronic communication, and information security. Students will evaluate and interpret current policies and regulations and formulate their own policies based on these models. Students will also discuss the implications for ethical decisions by technical professionals and leadership utilizing Case Studies and Problem-based learning. Students will learn best practices in ethical decision-making for the work place in this course. Prerequisite: CSC501 

Credits: 3

This graduate course provides students with an integrated and detailed approach to programming and software development principles. Students will understand the purpose of object-oriented software topics and pertinent software development principles. Topics included for this course focus on core programming concepts, data structures, methods, classes, and software models. 

Credits: 3

This graduate course provides students with a foundational knowledge in the design and analysis of algorithms. Students will make use of appropriate data structures. Complexity and analysis of algorithms will be completed focusing on worst case and average case, lower bounds, NP-completeness, and recurrences. Students will explore the complexity of appropriate searching, sorting, and graphing algorithms. Prerequisite: CSC505 Principles of Software Development 

Credits: 3

This graduate course provides students with a foundational knowledge in operating system concepts. Students will gain a detailed understanding of appropriate operating system constructs that involve OS abstractions and mechanisms. Students will also understand the constructs of multithreading and resource management in compute systems. 

Credits: 3

This graduate course provides students with an understanding of principles associated with Artificial Intelligence (AI). Students will determine how to utilize structures to represent graphs associated in data exploration. Students will gain an understanding of how to efficiently apply knowledge representation and techniques associated with AI reasoning. Topics that students will explore include techniques efficiently applying game theory, integer programming, continuous optimization, and probability analysis. 

Credits: 3

This Graduate course provide students with foundational knowledge in the areas digital image construction and processing. Students will explore topics associated with image formation, image acquisition, and image geometry. The course will expose students to the techniques required to efficiently analyze images for representation in applicable context scenarios. Students will apply image processing techniques for filtering and edge detection for image deconstruction. 

Credits: 3

This Graduate course provides students with an understanding of foundations concepts and theories in machine learning. Students will explore foundational topics that include: supervised and unsupervised learning, learning theory, reinforcement learning and adaptive control. Students will gain an understanding of applications of machine learning in areas of data mining, human computer interaction, natural language processing and computer vision. Prerequisite: CSC510 Artificial Intelligence 

Credits: 3

This Graduate course provides students with an overview of appropriate theories and models that are used to represent neural networks. Students will gain foundational knowledge in developing constructs to evaluate and represent components associated with neural networks and learning algorithms. Topics for this course include propagation, feedforward networks, perceptrons, and self-organizing networks. Prerequisite: CSC510. 

Credits: 3