Bachelor of Science in
Computer Science in Artificial
Intelligence Systems
4-week
COURSES
Year-round
enrollment
245K+ Alumni Worldwide
Overview
The Artificial Intelligence Systems concentration within the Undergraduate Computer Science program provides comprehensive coverage of the AI field, blending theoretical knowledge with practical skills. This program equips students to design, implement, analyze, and deploy intelligent systems by focusing on the core principles and techniques of AI. The concentration explores current technologies, techniques, and tools for developing AI solutions across various application domains, while fostering a critical understanding of the importance of explainability in AI systems and their societal impact.
Specialization Courses
Prerequisite: CYB 331; CYB 332
A comprehensive review of programming concepts using Python tailored for Artificial Intelligence applications, emphasizing the use of Python for statistical analysis and optimization problems. Coverage of Python data structures and related operations. An examination of object-oriented and functional-style programming in Python. The use of Python’s libraries to perform complex data manipulations and statistical calculations. Coverage of key optimization techniques used in Artificial Intelligence to solve real-world problems.
Prerequisite: CSC 448
An introduction to problem-solving using modern artificial intelligence techniques. The course explores the latest challenges in the theory, practice, applications, and implications of Al in the modern world with a focus on data science and machine learning algorithms and applications. Examines the role of heuristics in problem-solving. Concepts such as agents, production systems, and natural language communication are studied.
Prerequisite: CSC 448
An in-depth coverage of neural networks and deep learning focusing on three major types of networks: Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs). A study of foundational principles and techniques behind neural network architectures, including their design, training, and optimization. Application of neural networks and Python libraries to classification, regression, natural language processing, speech recognition and image recognition problems.
Prerequisite: CSC 453 and CSC 446
This course covers the foundational concepts and algorithms in reinforcement learning, including policy optimization, Q-learning, and deep reinforcement learning techniques. The course also examines generative AI, exploring techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). The review and use of current tools and libraries in practical projects.
Learning Outcomes
- Design, implement and deploy variety of AI systems
- Build and train AI models using neural networks
- Track and utilize current technical trends and approaches in the AI system development
- Discuss explainability and societal impact of AI system
Program Disclosure
Successful completion and attainment of National University degrees do not lead to automatic or immediate licensure, employment, or certification in any state/country. The University cannot guarantee that any professional organization or business will accept a graduate’s application to sit for any certification, licensure, or related exam for the purpose of professional certification.
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