M.S. in AiCE Degree Requirements


The Master of Science in Artificial Intelligence & Computer Engineering is a 96-unit program that provides students with a strong foundation of knowledge in AI and computer engineering that can be put to immediate use. The degree equips students with the expertise to pursue new frontiers in AI, Computer Engineering, and Innovation. Through our program, students develop their adroitness and gain insight and correlations that are crucial to facing critical challenges and applying the principles of AI across various industries. 
There are two tracks, Professional tracks and Thesis tracks available for students to choose either one to pursue

This track comprises 96 units; 84 units of graduate course work and 12 units of Research and Innovation.

Course work

84 units

Research and Innovation

12 units


96 units

This track comprises 96 units; 36 units of graduate course work and 60 units of Research and Innovation (Thesis-based). In this track of master’s, you are expected to conduct original research under a faculty advisor and make your contribution to the already available body of knowledge. Students in this track must defend their thesis as part of the graduation requirement.

Course work

36 units

Research and Innovation

60 units


96 units

Students pursuing the M.S. in AI & Computer Engineering will be able to:

Faculty Advising

Students with a bachelor’s degree and an interest in computer engineering or a related discipline with an interest in of AI are encouraged to apply to this program.

Curriculum Components (Course work)

Computer Vision (CMKL 41-385) – 12 Units

Software Engineering for AI-Enabled Systems (SE4AI) (CMKL 41-445) – 12 Units

AI Infrastructure & Accelerated Computing (CMKL 41-599) – 12 Units

Generative AI (CMKL 41-623) – (12 units)

Coding Bootcamp (CMKL41-600) – 12 Units
This course provides an intensive coding program that equips students with essential coding skills.

High Performance Computing for AI Application (CMKL41-605) – 12 Units
‍This course explores the infrastructure necessary to support AI applications, including both on-premise and cloud-based high-performance computing (HPC) setups. Students will learn the programming paradigms used to facilitate AI applications.

Natural Language Processing (CMKL41-611) – 12 Units
In this course, students will delve into natural language processing, focusing on techniques and algorithms used to understand and process human language using AI methods.

Foundation of Computer Systems (CMKL41-613) – 12 Units
‍This course emphasizes a programmer’s view of how computer systems run programs, collect information, and communicate. This encourages and helps students to become more effective and efficient programmers, particularly in handling issues of performance, portability, and robustness by teaching them the basic concepts underlying all computer systems (e.g., compilers, networks, operating systems, and computer architecture).

Introduction to Information Security (CMKL41-631) – 12 Units
‍The course provides the foundation of information security in details of some important technical and policy. The significant goal of the course is to encourage students to understand a security engineering perspective about information systems and consider technical, economic, and policy factors.

Artificial Intelligence and Future Markets (CMKL41-651) – 12 Units  
In this course, students will be placed into teams to examine the field of AI applications. They will present their findings to faculty and peers, identify areas with potential for AI development, and create a product proposal that will be developed over the next three semesters, leading to the Capstone Project.

Foundations of Software Engineering (CMKL 41-652) – 12 Units
In this course, students will get to understand computer software engineering paradigms that shaped the software industry over the past few decades. The course will emphasize the fundamental disciplines of computer software engineering together with engineering hands-on practices that crosscut systems, projects, and perspectives of the user.

AI Innovation (CMKL 41-654) – 12 Units    
In this course, students will learn how to establish and develop an enterprise, either as an intrapreneur or entrepreneur. They will create a business model and strategy for their team's product, with a focus on AI innovation.

‍Software Requirements and Interaction Design (CMKL41-658) – 12 Units
This course refers to computer software design challenges through integrating two disciplines: requirements engineering and interaction design. Students will get an understanding of how to combine user research, design-based ideation and validation, and requirements definition, within an agile software development process.

‍Introduction to Machine Learning for Engineers (CMKL41-661) – 12 Units
Machine learning has become a buzzword for over a decade now and has integrated itself deeply as one of the core pillars of digital transformation. This course makes you understand the definition of machine learning and emphasis AI computer engineering applications.

‍Hardware/Software Co-design (CMKL 41-701) – 12 Units
This course explores how software and hardware come together to implement computer systems. The course will be extremely hands-on, with weekly development cycles. Students will learn a new concept within the language/processor stack (e.g., parsing) and will be expected to implement it by the following week.

‍Introduction to Computer Security (CMKL41-730) – 12 Units
This course emphasizes on a principled introduction of defending against hostile adversary techniques in modern computer systems and computer networks. The topics are covered operating system security; network security, including cryptography and cryptographic protocols, firewalls, and network denial-of-service attacks and defenses; user authentication technologies; security for network servers; web security; and security for mobile code technologies (e.g., Java and JavaScript).  

Computer Architecture and Systems (CMKL41-742) – 12 Units
‍This course begins with a review of traditional, sequential computer architecture concepts. Moreover, it will discuss the end of the convention as a result of the end of the steady trend and Moore's Law, as well as several trends that these changes precipitated.  

Bayesian Statistics (CMKL 41-747) – 12 Units

‍Packet Switching and Computer Networks (CMKL41-756) – 12 Units
This course is intended to provide an understanding of the fundamental concepts in current and future computer networks.  

‍Network Management and Control (CMKL41-757) – 12 Units
This course teaches the fundamentals of broadband networks. Broadband networks differ from existing communication networks in many ways, and these issues will be addressed in the course.  

Introductions to AI Engineering (CMKL 41-763) – 12 Units

Multimodal Machine Learning (MMML) (CMKL 41-777) – 12 Units

Planning and Decision-making (CMKL 41-782) – 12 Units

Ethics in AI Engineering (CMKL 41-784) – 12 Units

Introduction to Deep Learning (CMKL 41-786) – 12 Units

Law and Ethics for AI  (CMKL41-762) - 12 Units   
This course provides an overview of legal principles relevant to computer advancements, including AI law and the formation of startups in this domain. Students will explore the legal and ethical considerations surrounding AI technologies.

Deep Learning (CMKL41-785) - 12 Units     
This course delves into deep learning techniques, covering advanced algorithms and methodologies used in training deep neural networks. Students will gain expertise in the field of deep learning.

Image and Video Processing (CMKL41-793) – 12 Units
This course focuses on signal processing techniques for 2D (images) and 3D (videos) signals. It extends 1D signal processing techniques and specializes them for image and video processing.

Research and Innovation

Research, Entrepreneurship and Innovation (CMKL41-900) – 12 Units
This unique course for AiCE program introduces students to explore the connections between research, entrepreneurship and innovation. Students will be introduced to industries and tech communities. 

Research and Development (CMKL41-910) – 36 Units Students in AiCE programs will have the opportunity to participate in real-world supervised research and development projects.   

Internship for Graduate (CMKL41-995) – Variable
 Experiential learning experiences are key educational possibilities for graduate students in the Artificial Intelligence and Computer Engineering department. An internship, which is usually conducted during the summer, is one such alternative. 

Graduate Teaching Internship (CMKL41-999) – 12 Units The Teaching Internship for AI and Computer Engineering MS Students represents the capstone or culminating experience at CMKL University in the preparation of prospective lecturers as knowledgeable, reflective practitioners and emerging leaders who conduct themselves ethically and professionally.


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