Artificial Intelligence+ Courses | 2026-2027 Academic Catalog | SIU

Introduction to Artificial Intelligence (AI) explores the basic theory and practice of creating and working with intelligent systems. The course covers core concepts, major application domains, and AI tools. Students will gain an introductory understanding of key areas including search algorithms, machine learning, deep learning, intelligent agents, and prompt engineering. Emphasis will be on learning through practice with simple code examples, AI tools, and prompt engineering interactions with AI agents. We prevent students from over-relying on AI by requiring AI usage disclosure including declaration of (a), AI tools used and extents of their usage, (b) specific shortcomings with available AI tools, and (c) lessons learned from using AI tools.

Credit Hours: 3

This course offers an engaging introduction to programming through the lens of Artificial Intelligence (AI). Designed for beginners, it blends the fundamentals of Python programming with intuitive AI concepts to create a practical, hands-on learning experience. Students will learn core programming constructs while applying them to basic AI tasks such as search, logic, and data handling. The course emphasizes algorithmic thinking, problem-solving, and writing clean, efficient code, preparing students for further study in computer science or AI. We prevent students from over-relying on AI by requiring AI usage disclosure including declaration of (a), AI tools used and extents of their usage, (b) specific shortcomings with available AI tools, and (c) lessons learned from using AI tools. Prerequisite: Mathematics 111 or equivalent with a grade of C or better.

Credit Hours: 4

Introduction to topics relevant to the study of computer science including: number systems, sets, sequences, summations, logic and truth tables, proofs, functions, relations, matrix operations, combinations, permutations, counting techniques, discrete probability, algorithmic complexity, recurrence relations, Boolean algebra, simple combinational circuits, simplification techniques. Prerequisite: Mathematics 111 or equivalent with a grade of C or better.

Credit Hours: 4

Introduction to topics relevant to the study of computer science including: number systems, sets, sequences, summations, logic and truth tables, proofs, functions, relations, matrix operations, combinations, permutations, counting techniques, discrete probability, algorithmic complexity, recurrence relations, Boolean algebra, simple combinational circuits, simplification techniques. Prerequisite: (CS 202 or AIP 202) and (CS 215 or AIP 215) with a grade of C or better.

Credit Hours: 4

This course provides a basic introduction to probability and statistics as well as related computational approaches. Topics include basic probability models, combinatorics, random variables, discrete and continuous probability distributions, statistical estimation and hypotheses testing, confidence intervals and linear regression. Some selected computational approaches for statistical problems such as simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and methods in inference will also be discussed. The R language will be used for programming assignments. Prerequisite: MATH 106 or MATH 108 with a grade of C or better.

Credit Hours: 3

This course develops effective writing, reading, presentation, and oral communication skills for computer science professionals. Emphasis is placed on evaluating and communicating technical material clearly to diverse audiences, including stakeholders and team members. Students explore professional ethics and responsibilities in computing, with attention to societal, legal, and sustainability impacts. The course examines emerging ethical, safety, and security challenges in technologies such as Artificial Intelligence (AI), preparing students to engage with complex issues in responsible and trustworthy computing. Assignments and discussions are drawn from technical sources, case studies, and real-world scenarios related to the history, practice, and future of the discipline. Prerequisite: CS 201 or AIP 201 or CS 202 or AIP 202 with a grade of C or better.

Credit Hours: 3

This course builds on the foundational concepts introduced in Fundamental Programming with AI using Python. It reinforces key Python and AI topics while introducing essential tools and libraries used in modern AI programming workflows. Students will gain practical experience with numerical computing using NumPy, data manipulation using Pandas, and data visualization with Matplotlib. The course also introduces high-level overviews of PyTorch and TensorFlow to prepare students for deeper studies in Machine Learning. The emphasis remains on programming fluency, structured thinking, and the ability to use the Python ecosystem effectively in AI contexts. Prerequisite: CS 220 or AIP 220 with a grade of C or better.

Credit Hours: 3

Intensive study of the fundamentals of data structures and algorithms. Presents the definitions, representations, processing algorithms for data structures, general design and analysis techniques for algorithms. Covers a broad variety of data structures, algorithms and their applications including linked lists, various tree organizations, hash tables, strings, storage allocation, algorithms for searching and sorting, and a selected collection of other algorithms. The course will focus on implementation and analysis of algorithms, as well as how Artificial Intelligence (AI) can be utilized when performing comparative analysis. Use of AI tools will be allowed and will be supervised. Prerequisite: AIP 220 or CS 220 with a grade of C or better.

Credit Hours: 3

This course introduces various AI tools used in industry and research. Students will explore AI-driven software, frameworks, and APIs for data analysis, automation, machine learning, and others. The course emphasizes practical implementation, ethical considerations, and emerging AI applications. The course also reviews contemporary AI tools to prepare students for implementing real-world applications. Prerequisite: CS 202 or AIP 202 with a grade of C or better.

Credit Hours: 3

This course introduces the foundations and applications of Large Language Models (LLMs). The course covers core concepts such as embeddings, transformer architectures, text classification, semantic search, and multi-modal LLMs. Students will also gain hands-on experience in fine-tuning and evaluating pre-trained models for real-world language understanding tasks. Prerequisite: CS 202 or AIP 202 with a grade of C or better.

Credit Hours: 3

This course introduces practical and conceptual foundations of prompt engineering for large language models (LLMs). Students will explore a range of prompting techniques, from basic to advanced, and learn how to design structured, effective prompts for a variety of tasks. Emphasis is placed on iterative design, prompt evaluation, and adapting prompts to specific contexts and goals. The course covers both text and image generation, and highlights real-world applications in writing, coding, planning, education, and more. Prerequisite: AIP 201 or CS 201 or AIP 202 or CS 202 with a grade of C or better.

Credit Hours: 3

This advanced course builds upon foundational AI tools knowledge and delves into customized applications, automation pipelines, integration with APIs, and development of intelligent systems using pre-trained models. Students will work on case studies in domains-specific such as education, healthcare, creative arts, business automation and others. The course also reviews contemporary AI tools to prepare students for implementing real-world applications. Prerequisite: AIP 340 with a grade of C or better.

Credit Hours: 3

This course introduces the fundamentals of computer vision, enabling students to understand theories, algorithms and practical implementation that allow machines to interpret and process visual data. Topics include image formation, feature extraction, image classification, object detection, segmentation, motion analysis, and deep learning for vision. The course also reviews contemporary tools and techniques for computer vision to prepare students for real-world applications. The Python language will be preferred for programming assignments. Prerequisites: (AIP 202 or CS 202) and (AIP 280 or CS 280) with a grade of C or better.

Credit Hours: 3

Selected advanced topics from the various fields of Artificial Intelligence. Prerequisite is determined by instructor.

Credit Hours: 3

This course consists of diverse presentations by faculty, students, and invited speakers from industry, and prepares students for AIP 499. Students will select and plan a real-world team project under advisement of a program faculty, and will present a project proposal. Prerequisite: Completion of or concurrent enrollment in at least two other 400-level AIP courses. Restricted to 4th-Year standing in AIP.

Credit Hours: 2

This course is a continuation of AIP 498. Students will design, implement, document, deploy, and present a group project applying AI techniques. Prerequisite: AIP 498.

Credit Hours: 3