Preparing for a Career in Artificial Intelligence and Machine Learning Through Undergraduate Programs in Computing at UL Lafayette
At the University of Louisiana at Lafayette, we believe that preparing tomorrow’s artificial intelligence (AI) and machine learning (ML) innovators demands more than teaching algorithms alone. It requires fostering a deep understanding of how intelligence works, learning to build and evaluate complex intelligent systems, and cultivating the foresight to ensure these technologies serve humanity responsibly.
Our undergraduate computing programs give students the foundations they need to excel in this dynamic field. Through an integrated journey that blends rigorous theory, hands-on engineering, and thoughtful exploration of AI’s societal impacts, students develop the skills and perspectives essential for the AI and ML careers of tomorrow.
Students begin by exploring the rich foundations of computing that make AI possible. Their journey starts in CMPS 150 – Introduction to Computer Science, which establishes a firm foundation in programming and problem-solving. Here, they develop proficiency in Python, the primary language for modern AI and ML, while exploring the core concepts that will prepare them for advanced work with intelligent systems.
This journey continues in CMPS 260 – Introduction to Data Structures & Software Design, where students explore how to engineer intelligent systems using robust, object-oriented languages like Java. By modeling complex problems as a system of interactive objects, the course challenges students to move beyond simple programs and design software that can make decisions, demonstrating the core principles of algorithmic strategy and planning in AI.
This crucial foundation in problem-solving is solidified in CMPS 261 – Advanced Data Structures & Software Design, where students master the advanced data structures that are critical for implementing AI algorithms. They learn to analyze the performance of structures like graphs and trees, ensuring the solutions they engineer are not just correct but also highly efficient and scalable for real-world challenges. In each of these first three courses, students learn to apply sound software design principles and recognize the value of writing clear, maintainable code that supports both effective functionality and long-term adaptability.
Students dive directly into the field in CMPS 320 – Introduction to AI & ML, where they study how intelligent systems solve problems through search, reasoning, and planning, while gaining practical exposure to supervised and unsupervised learning — including decision trees, neural networks, clustering, and dimensionality reduction. They also learn to engineer effective data and feature representations, with an early introduction to natural language processing.
As part of the core theory sequence, CMPS 340 – Design and Analysis of Algorithms and CMPS 341 – Foundations of Computer Science emphasize pen-and-paper reasoning to build lasting intuition. In CMPS 340, students analyze efficiency and correctness through recursion, dynamic programming, and graph algorithms, learning to justify their choices with clarity and precision. CMPS 341 deepens this foundation with logic, sets, and proof techniques, reinforcing clear thinking and communication across disciplines. This human-centered approach prioritizes reasoning that is not only rigorous but also accessible. Students are thus prepared to design systems whose structure and purpose can be clearly understood, critiqued, and improved by others.
Our students push the frontier of modern software development by embracing the transformative power of AI. In CMPS 357 – Accelerated Software Development Using AI Tools, they learn to integrate AI into modern engineering workflows, using intelligent tools to accelerate prototyping, debugging, and automated testing. The course emphasizes critically selecting and evaluating AI outputs, balancing the speed of rapid AI-generated code with the essential human judgment needed to manage large, complex codebases.
This progression continues in CMPS 420G – Artificial Intelligence, where students deepen their expertise in agentic AI, studying how autonomous systems make adaptive decisions. They explore advanced search and planning algorithms, game-playing strategies, and knowledge representation systems that allow machines to reason about complex environments. The course connects foundational theory to modern practices, including deep learning and evolutionary algorithms.
As they progress to CMPS 422 – Machine Learning, students engage deeply with the engineering aspects of learning systems at an advanced level in their senior year. They learn to mathematically formulate machine learning problems, apply foundational principles such as bias-variance tradeoff and cross-validation, and build models using supervised and unsupervised techniques, including regression, decision trees, k-NN, clustering, and neural networks. The course balances theory with practical implementation, requiring students to design models in Python and address real-world challenges like model complexity, training efficiency, dimensionality reduction (e.g., PCA, LDA), and memory optimization. Students also explore metrics-driven evaluation and develop hands-on experience with model tuning and validation. By the end of the course, they are equipped to design, implement, and present scalable ML solutions with a solid understanding of both performance and reliability in practical applications.
Students continue learning to apply these advanced AI and ML skills in specialized upper-level courses that connect machine intelligence to the core areas of computing. In CMPS 460 – Database Management Systems, they engage directly with the rise of Large Language Models, where they learn to work with and critically evaluate AI chatbots, correcting and improving their generated solutions for technical tasks involving relational algebra and SQL query optimization. This work prepares them to be discerning professionals, equipping students with the critical judgment needed to harness the power of generative AI tools effectively.
Their hands-on learning at the frontier of applied ML continues through CMPS 455 – Operating Systems, where our students tackle one of the ultimate challenges in computing: optimizing system performance. The course serves as a critical synthesis of their knowledge in both systems and AI, challenging them to architect and deploy supervised learning models to predict the notoriously complex behavior of multi-core CPU schedulers. By building and validating these predictive systems, students not only deepen their practical ML expertise but also gain invaluable experience at applying it to mission-critical, low-level performance tuning.
Their journey from theory through implementation culminates in CMPS 490 – Senior Project, where students pair one-on-one with a faculty mentor or in teams with the help of industry professionals to design and build a substantial project of their choosing. Working closely with domain experts teaches them to step out of their own perspective and approach problems through the lens of someone who has thought deeply about the field. Those passionate about AI and ML often develop their largest undergraduate systems here, showcasing the creativity and technical expertise that sets them apart as emerging professionals.
Beyond technical mastery, our students graduate prepared to thoughtfully consider how AI shapes the world. In CMPS 310 – Computers in Society, they examine how intelligent systems intersect with ethics, law, and social change, exploring topics like algorithmic bias and the impacts of AI automation on the nature of work. Through case studies and an independent research paper, they build a foundation that keeps them adaptable and ethically grounded as new AI trends transform how we live, work, and create.
Graduates of UL Lafayette’s computing programs emerge as well-rounded, forward-thinking professionals, ready to thrive as AI engineers, data scientists, software developers, or advanced researchers. They stand out not only for their technical expertise but for a principled, human-centered perspective, prepared to lead in a world increasingly shaped by intelligent systems.
Our graduates put these skills to work across Louisiana and beyond. Locally, they’ve helped drive innovation at CGI, Perficient, Techneaux, Stuller, McIlhenny, IBM in Baton Rouge, and GE in New Orleans. Others have launched careers with global leaders like Google, Microsoft, Facebook, HP, and Intel, applying AI and ML to tackle some of today’s most pressing challenges. Many also contribute their expertise at national laboratories and across U.S. federal and state agencies, bringing intelligent systems to critical public and research missions.
Whether your goal is to push the boundaries of machine learning, build systems that adapt and learn on their own, or ensure tomorrow’s AI technologies are fair and transparent, you’ll find your launchpad here.