Applied Artificial Intelligence

Students are introduced to artificial intelligence and explore different technologies utilizing AI concepts and skills widely accepted within the AI industry. Topics will include AI problem-solving methods, knowledge representation, and classification algorithms. The course will also discuss ethical considerations of AI and a survey of emerging technologies across various industries. Students will gain hands-on experience performing simple exercises using modern AI tools. Students will develop a college success strategy to construct academic plans and career goals. Three lecture hours per week. An instruction support fee applies. General competencies met: Critical Thinking. First-Year Experience. 3 Credits.

This course introduces the core concepts of machine learning, offering a comprehensive overview of the techniques and algorithms used in this rapidly growing field. Students will learn about the foundation of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Students will gain hands-on experience implementing machine learning algorithms using popular programming languages and libraries. Students also evaluate the ethical implications of AI solutions. Prerequisite/Corequisite: CIS 153 Python or permission of the instructor. Four lecture hours per week. An instruction support fee applies. General competencies met: Information Literacy. 4 Credits.

This course provides an introduction to computer vision, a discipline that involves teaching computers to interpret visual data. Students will learn fundamental computer vision concepts such as image preprocessing, feature detection and extraction, and object recognition. This course also covers practical aspects of computer vision, including implementing computer vision algorithms using popular programming languages and libraries. Students will gain hands-on experience with real-world applications of computer vision. Students also evaluate the ethical implications of computer vision solutions. Prerequisite: CAI 120 Machine Learning Foundations or permission of the instructor. Four lecture hours per week. An instruction support fee applies. General competencies met: Information Literacy. 4 Credits.

Students are introduced to the field of Natural Language Processing (NLP), focusing on techniques and methods for processing and analyzing human language data. Students will learn fundamental NLP concepts such as text processing, sentiment analysis, text classification, and summarization. Students will gain handson experience implementing various NLP algorithms and techniques using popular programming languages. Students also discuss the ethical implications of using NLP solutions in modern society. Prerequisite: CAI 120 Machine Learning Foundations or permission of the instructor. Four lecture hours per week. An instruction support fee applies. General competencies met: Information Literacy. 4 Credits.

Students will explore the foundational principles of artificial intelligence and its application in business analytics. Students will examine the role of data science in extracting valuable insights from complex data sets and how these insights can inform strategic decisions. Students will gain hands-on experience using various data science methodologies to solve real-world business problems. Students will also evaluate the ethical guidelines and governance frameworks for deploying AI responsibly. Prerequisite: CAI 120 Machine Learning Foundations or permission of the instructor. Four lecture hours per week. An instruction support fee applies. General competencies met: Information Literacy. 4 Credits.

The capstone course in applied AI is an integrative, project-based learning experience that challenges students to apply their acquired knowledge and skills to realworld problems, emphasizing hands-on experience, critical thinking, and innovative solution development. Students will work individually or in teams to identify a problem area where AI technologies have an impact. Students will design, develop, and implement a comprehensive solution incorporating various aspects of machine learning, NLP, computer vision, and/or data analytics. The course culminates with a student presentation to peers, faculty, and industry professionals. Four lecture hours per week. Prerequisite: CAI 120, CAI 121, CAI 122, and CAI 123 or permission of the instructor. An instruction support fee applies. General competencies met: Information Literacy. High Impact Practice: Capstone. 4 Credits.