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Harnessing Artificial Intelligence: An Inside Look at the TXWES Curriculum

At Texas Wesleyan University, our master's degree program in computer science is an accelerated, graduate-level sprint that will give you the advanced technical knowledge you need to compete in the rapidly evolving world of artificial intelligence (AI).

Overview of AI-Focused Coursework

The core courses of Texas Wesleyan's online Master’s of Computer Science (MS) degree provide you with the foundation you need to progress in your technology career.

Core Concepts and Foundational Classes

In 2022, Open AI co-founder and founder of the safe-AI startup Safe Superintelligence Inc. Ilya Sutskever posted, "It may be that today's large neural networks are slightly conscious." Other top AI researchers immediately disputed Sutskever's claim, which continues to be debated.

Whether conscious or not, AI is revolutionizing all industries and many walks of life. Our graduate program in computer science — with the option to be completed in 12 months or 15 months — lays the groundwork for understanding the hardware, programming languages and theories underlying the AI revolution.

Beginning with the Computer Networks and Principles of Software Engineering course, you'll also engage in independent learning and research in Computer Architecture, Operating Systems and Distributed Systems. This will provide a basis for understanding the diverse approaches that the AI industry is taking to implementing AI, machine learning (ML) and related technologies.

Foundational courses offer a deep understanding of how AI systems run, before you advance to the technologies underlying large-scale data analytics, robotics and advanced AI/ML applications.

Advanced Machine Learning and Data Analytics

In addition to courses on computer architecture and fundamental principles of software engineering, you'll dive into graduate-level courses in Machine Learning and Foundations of Algorithms.

While not a specific artificial intelligence degree, Texas Wesleyan's curriculum includes courses that provide a foundation for AI career paths — not just within AI-focused companies but also through developing artificial intelligence projects, AI applications in business and working in firms that use a variety of AI programming languages.

Hardware and Computer Science

You will also learn about computer hardware systems (including GPUs, TPUs and system design), giving you a fundamental understanding of the architecture of neural networks and efficient operation of the required hardware.

Industry leaders like Google and NVIDIA hire engineers who understand the interactions between machine learning, deep learning and the hardware that's required to deploy these technologies. The MS in Computer Science through Texas Wesleyan offers the education and experience to enable you to work on AI hardware or software optimization, designing machine learning frameworks.

Machine Learning Curriculum

You'll dive into CSC 6420 - Machine Learning, which covers the math backbone of ML: probability theory, the basis of how algorithms aid in decision-making. The course will guide you through model selection and decision theory, too, enabling you to work through uncertain problems and select the best ML model for your purpose.

Machine learning typically works with high-dimensional data, and the more dimensions of data that are involved, the greater the challenge in working with it (sometimes called the "curse of dimensionality”). The machine learning course will guide you through feature selection and other ways to help you overcome such challenges, as well as prevent overfitting or underfitting of models when analyzing complex, massive sets of data. Texas Wesleyan's graduate course in ML will also offer experience in linear regression and classification, essential concepts in the field.

Additionally, you'll work with mixture models and expectation models, learning how to create algorithms for clustering or density. Graphical models such as Bayesian Networks and Markov Random Fields will also be covered, offering you tools for working with probabilistic reasoning and image processing. Finally, CSC 6420 offers a deep dive into neural networks and deep learning, with supervised learning that will allow you to experience applied machine learning.

Mastering Algorithmic Thinking

CSC 6360, Foundations of Algorithms, has a core focus on the math and efficiency of algorithms, which provide the essential tools to process data and solve problems using machine learning and artificial intelligence.

First, you'll learn about computational complexity and Big-O notation: limiting a function when the argument tends toward one specific value or infinity. Given the vast volumes of data involved in AI, these tools will offer you ways to optimize time spent performing various operations as well as space required for data. The course will also cover important strategies like sorting and searching through quicksorts and binary searches, how to break problems down through methods like divide-and-conquer and important optimization techniques (like Kruskal's algorithm).

This course covers dynamic programming concepts like fitting the shortest paths into an optimal substructure and minimum spanning trees (MST). CSC 6360 balances theory offered by formal proofs with applications that enable you to implement algorithms efficiently in hands-on projects. Some of the applications, including deep learning algorithms, that rely on concepts you'll learn in this course include robotics, game AI and autonomous driving.

Hands-On Projects and Research Opportunities

Your computer science online program at Texas Wesleyan University offers an opportunity for hands-on projects in AI and machine learning. What’s more, you’ll have the chance to conduct research alongside the Computer Science department's experienced, capable faculty.

Collaborations With Industry Partners

Texas Wesleyan’s Department of Computer Science regularly invites industry-relevant speakers to campus through its student engagement programs. Faculty consistently endeavor to connect students with local organizations, including the Fort Worth Independent School District (ISD), banks and major employers. The MS in Computer Science program can connect you to opportunities with not just major tech industry partnerships but also other organizations that will need your expertise and capabilities in research collaboration as they transition to the AI-informed future.

According to McKinsey & Company's QuantumBlack AI division, "Getting real value out of AI requires transformation, not just new technology … It’s also a potentially expensive transformation, requiring intensive use of sometimes scarce resources and talent." The computer science master’s degree and collaborations with industry partners will enable you to become the resource and talent required in nearly every industry, as well as the AI companies themselves.

Capstone Projects for Real-World Application

Your MS in Computer Science program will conclude with a capstone project in a real-world application. For example, you could investigate a potential benefit of AI in education or create innovative machine learning projects and reinforcement learning applications.

Take inspiration from one of Texas Wesleyan's computer science alumna, Ana Aleksandric, who was awarded the Technology Informatics Guiding Education Reform (TIGER) Scholars Informatics Internship while serving as a doctoral-level researcher at the University of Texas at Arlington, using health informatics and predictive analytics to fight COVID-19 disinformation.

Tools, Technologies and Platforms

Data-driven decision-making has been around for a long time, and AI is revolutionizing how decisions are made (and even, in some cases, what the decisions are about). McKinsey Senior Partner, Alexander Sukharevsky, says, "As organizations become more fluent with AI, it will essentially become embedded in all functions."

Your CS master's degree at Texas Wesleyan will cover the most common AI tools and technologies — from chatbots and conversational AI (like xAI's Grok or ChatGPT) to image generation tools, voice and speech tools like Siri and Alexa, data analysis and prediction tools like Tableau and TensorFlow, and content recognition tools like Google Lens or the facial recognition programs in your cell phone.

Rather than focusing on a single platform, you'll be able to work with programs such as:

ChatGPT and GPT models like 4o
Art generators like DALL-E
Microsoft Copilot
Google's Gemini (formerly Bard)
Vertex AI
xAI's Grok

Popular AI Libraries and Frameworks

What makes the AI platforms run? Many AI libraries and frameworks are open-source. From the massive ecosystem of Google's TensorFlow to Meta's PyTorch, you will find libraries of frameworks that today's AI researchers, engineers and developers use to test and deploy AI programs. Other frameworks and libraries include Scikit-learn, which is focused on machine learning, and natural language processing (NLP) resource Hugging Face Transformers.

You can also explore lesser-known but specialized or lightweight frameworks like MXNet (backed by Amazon), Caffe (a slightly older framework that can be used for computer vision projects), FastAI and XGBoost.

Cloud-Based Environments for Data Processing

Big data and artificial intelligence call for scalable, cost-effective environments that can handle the amount of power, storage and speed required to make them feasible. Cloud environments often will come with ML frameworks pre-installed, like TensorFlow and PyTorch. Your MS in Computer Science program at Texas Wesleyan can help familiarize you with major cloud-based environments like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform.

Preparing for the AI-Driven Future

Now is the time to prepare for the future of AI, with companies of all sizes beginning to implement AI solutions. A 2025 McKinsey survey of nearly 1,500 companies in 101 nations found that, while larger companies were implementing AI and ML solutions at a greater rate than smaller ones, all of the surveyed businesses said their AI efforts were in the beginning stages.

Building a Marketable Skill Set

You need to possess a marketable skill set to move into AI and ML at a high level. Learning the required languages like Python, along with other languages such as Java, R and C++, will also be beneficial. In addition, you’ll want to understand algorithms, regression, classification, decision trees and neural networks. Gaining additional skills in math, data handling, predictive analytics and deploying AI/ML models will benefit you as you enter the marketplace.

Career Paths and Industry Outlook

Graduates of Texas Wesleyan University's computer science program are working for companies as diverse as PricewaterhouseCoopers (PwC), First Command Bank and Fort Worth ISD. Thousands of new AI/ML engineers, researchers and other professionals are needed: The United States Bureau of Labor Statistics (BLS) projects more than 350,000 job openings in the sector annually between 2023 and 2033.

Pursue Your MS in Computer Science Online to Learn More About AI

When it comes to artificial intelligence in education, the MS in Computer Science and accompanying artificial intelligence curriculum at Texas Wesleyan University can offer the necessary insight into emerging trends in artificial intelligence to establish a rewarding career in this field. Get in touch today to learn more about this 100% online degree program.