The Artificial Intelligence: Theory, Methods, and Applications (AITMA) minor offered by the Department of Electrical and Computer Engineering provides students with a rigorous, engineering-focused foundation in AI. The minor integrates AI theory, methods, and applications, giving students a comprehensive understanding of the field. AI theory explains how intelligence can be modeled, AI methods provide the algorithms and techniques that make these models work, and AI applications allow students to implement these algorithms in real-world contexts. Students gain the skills to analyze, design, and apply AI across a wide range of systems and sectors, including embedded devices, robotics, IoT, machine vision, healthcare, finance, retail, manufacturing, agriculture, sustainability, transportation, and cybersecurity.

The AITMA minor is designed to be multidisciplinary and accessible to students from across the university, including those in liberal arts and sciences, business, and other non-engineering majors. While no prior engineering background is required to begin the program, students should expect to complete upper-level ECE coursework as part of the minor. Students from all disciplines are encouraged to apply AI concepts within their own fields of study.

Students may count up to one AI-related course from a department outside of Engineering (ENGR or ECE) toward the minor. This flexibility allows students to connect AI with their primary area of interest. Examples include a major-specific AI course, a course focused on the ethical and societal implications of AI, or an upper-level probability or statistics course.

The minor includes one required course, ENGR:3110 Introduction to AI & Machine Learning, which is designed for students from a variety of academic backgrounds. Depending on a student’s major, one or both prerequisite courses may also count toward the minor requirements. For example, students whose majors do not require a programming course may count ENGR:1300 Introduction to Engineering Computing toward the minor. Similarly, students whose majors do not require a matrix algebra course may count an approved matrix algebra course as their supporting course.

Course requirements

The minor requires 15 semester hours (s.h.).

  • In addition to the required course, students must complete electives from at least two of the following categories: Theory, Methods, and Applications.
  • Optionally, students may count one elective from the Support category.
  • Students may count one AI-related course that is not an ECE or ENGR course towards the minor.
  • Through choice of electives, students can tailor the minor to align with their academic interests and career goals.

Required core courses (3 sh):

  • ENGR:3110 Intro to AI & Machine Learning in Engr1, P: ENGR:13002, C: MATH:25503

Theory:

  • ECE:5200 (previously ECE:5450) Machine Learning
  • ECE:5225 (previously ECE:5455) Statistical Foundations of Inference and Machine Learning
  • ECE:5240 Deep Learning Theory

Methods:

  • ECE:5215 Applied Machine Learning
  • ECE:5250 Large Language Models
  • ECE:5485 Intelligent Vision and Image Understanding
  • Other: AI methods course from another department, approved by ECE Undergraduate Committee

Applications:

  • ECE:5230 Generative AI Tools: ChatGPT and Beyond
  • ECE:5290 Artificial Intelligence: Experiential Learning
  • ECE:5550 Internet of Things
  • ECE:5830 Software Engineering Project
  • ECE:5845 Modern Databases
  • Other: AI applications course from another department, approved by ECE Undergraduate Committee

Support:

  • ECE:5320 High Performance Computer Architecture
  • ECE:5420 Power Systems and Renewable Energy
  • CS:3980 Topics in Computer Science I: Ethics in Artificial Intelligence
  • AI Ethics course
  • Upper-level Probability course4

Notes:

  1. ENGR:3110 may be replaced by a 5000-lvl ECE theory, method, or application course.
  2. Majors that do not require a programming course may count ENGR:1300 Introduction to Engineering Computing towards the minor.

  3. Majors that do not require a matrix algebra course may count one of the following as their support course.

    a. MATH:2550 Engineering Matrix Algebra, 2 s.h. (Students will need one additional sh to earn minor.)

    b. MATH:2700 Introduction to Linear Algebra

  4. Majors that do not require a probability course may count one of the following as their support course.

    a. STAT:2020 Probability & Stats for Engr & Phys Sci

    b. STAT:3120 Probability and Statistics

    c. ECE:3995 Introduction to Probability and Statistics

Other requirements
  • Students must earn a GPA of at least 2.00 in all coursework applied to the minor.
  • No course taken Pass/Nonpass may be used toward the minor.
  • Enrollment in some courses for the minor may require prerequisites that will not count toward the minor.
  • Students must be enrolled as degree-seeking undergraduates at the University of Iowa to pursue the minor.
  • A maximum of 3 s.h. of transfer credit will be accepted toward minor.

ECE course descriptions and prerequisites
  • ENGR:3110 Intro to AI & Machine Learning in Engr
    Introduction to artificial intelligence (AI), machine learning, data science, and data driven problem solving across all engineering disciplines; topics include supervised and unsupervised learning, clustering, heuristics, feature selection, ethics of AI—fairness and privacy issues, and performance evaluation; first in a series. Prerequisite Courses: ENGR:1300; Corequisites: MATH:2550; Requirements: practical knowledge of programming, rudimentary understanding of probability concepts, and sophomore standing
  • ECE:5200 (previously ECE:5450) Machine Learning
    Fundamentals of machine learning theory including regression, classification, neural networks, clustering, and principal component analysis; engineering applications. Prerequisite Courses:  ECE:2400 or BME:2200
  • ECE:5215 Applied Machine Learning
    Introduction to deep learning, covering convolutional neural networks, backpropagation, optimization, supervised training of classifiers and regression models, and representation theory; emphasis on designing and implementing algorithms in PyTorch, including practical tips, tricks, and normalization approaches; provides in-depth understanding of machine learning theory and algorithms, with a brief overview of support vector regression and classification, kernel methods, and their connections to deep learning; introduces unsupervised learning methods, including principal component analysis, clustering, autoencoders, and generative adversarial networks. Prerequisite Courses:  ECE:2400 or BME:2200
  • ECE:5225 (previously ECE:5455) Statistical Foundations of Inference and Machine Learning
    Basic strategies to cope with noise in measurements with three objectives at core of most machine learning tasks—hypothesis testing (where one must choose between various hypotheses), parameter estimation (where multiple parameters whose values define how a system will behave must be estimated from noisy measurements), and filtering (where a noisy music signal must be cleaned up); topics include probability and statistics, random variables and signals, hypothesis testing, parameter estimation, discrete- and continuous-time random processes, and optimal filtering; assignments, written exams, and projects. Prerequisite Courses: STAT:2020 and ECE:2400
  • ECE:5230 Generative AI Tools: ChatGPT and Beyond
    Hands-on, project-based; explores tools like large language models (ChatGPT, Copilot), image generators (Midjourney, DALL-E), video tools (Sora, Runway), and audio/music synthesis; use these technologies across domains—from engineering to art—through labs, assignments, and projects; examines ethical and legal issues, including bias, privacy, and societal impact; critically explore how artificial intelligence reshapes identity, institutions, and what it means to be human. Recommendations: ENGR:2730 and ENGR:3110
  • ECE:5240 Deep Learning Theory
    Focus on the statistical foundations and core concepts of deep-learning-based machine learning; address the challenges of deep learning's "black box" nature by exploring theoretical perspectives on generalization performance, limitations, and adversarial vulnerability; emphasize developing intuition for how and why deep learning works, guiding future improvements, and identifying open research problems in the field. Prerequisite Courses: STAT:2020
  • ECE:5250 Large Language Models
    Introduction to modern techniques in natural language processing (NLP), with emphasis on language models; recent advances in language modeling have enabled systems that translate text, answer questions, and engage in spoken conversation; topics include language modeling, representation learning, deep learning approaches for NLP, text classification, sequence tagging, machine translation, and Transformers, among others. Recommendations: ECE:2400 or BME:2200 prior to enrolling
  • ECE:5290 Artificial Intelligence: Experiential Learning
    Practical experience in designing and applying artificial intelligence/machine learning methods and pipelines; hands-on work with real-world, open-ended applications; work with real data, select effective approaches, evaluate performance, and collaborate with experts from fields like medicine, art, and education; builds on prior artificial intelligence/machine learning knowledge; emphasizes interdisciplinary projects, tool development, and application-oriented skills beyond traditional lecture-based learning. Prerequisite Courses: ENGR:2730, Requirements: ECE:5200 or ECE:5215 or ECE:5230 or ECE:5240 or ECE:5250 or ECE:5485 or other relevant coursework
  • ECE:5485 Intelligent Vision and Image Understanding
    Advanced concepts and practical challenges in intelligent vision, image processing, and understanding; emphasis on developing problem-solving skills and engineering intuition; machine learning is integrated with systems theory to build on prior knowledge; apply intelligent image concepts and engage with advanced texts and research; topics may include image enhancement and filtering, edge/line/corner detection, maximally stable extremal regions, conventional and artificial-intelligence-based segmentation, registration, mathematical morphology, and wavelet theory. Prerequisite Courses: ECE:2400 or BME:2200
  • ECE:5550 Internet of Things
    Internet of things (IoT) describes the evolution of the internet to intelligent devices, sensors, actuators, controllers, and other types of internet-enabled components; soon, IoT-based applications will enable seminal advances in a wide range of areas including health and lifestyle, transportation, smart cities, environment, energy, agriculture, and industry; topics include IoT logical and physical structure, IoT-enabled internet services, IoT devices/platforms/endpoints, IoT application domains, IoT security and privacy issues, and IoT data analytic; case studies and projects focused on design and implementation of a working IoT application. Prerequisite Courses: ENGR:2730