# Artificial Intelligence (AI) courses Chaffey College · 2026-2027 Catalog · 9 courses Source: https://chaffey.curriqunet.com/catalog/iq/19209/20264/18796/20090/20561 "Offered" lists sections in this catalog year's class schedules: Summer 2026, Fall 2026, Spring 2027. ### AI 1 — Artificial Intelligence: Prompt Engineering and AI Systems - **Units:** 3 - **Hours:** 48-54 Lecture - **Transfers to:** CSU - **Offered:** Fall 2026, 2 sections listed as AI-1 (/c/chaffey-college/schedule/fall-2026/ai.md); Spring 2027, 2 sections listed as AI-1 (/c/chaffey-college/schedule/spring-2027/ai.md) - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course introduces students to the fundamentals of prompt engineering, practical use of generative AI tools, and foundational knowledge of the information systems and infrastructure that power AI technology. Students will survey various AI tools, produce effective prompts, evaluate AI outputs, and explore how these technologies are used across industries such as business, education, media, and creative fields. Students will be introduced to input/output processing, system components, various technology, networking, database queries, security, ethical considerations, and other topics. Students will gain hands-on experience designing, evaluating, and applying prompts to real-world tasks using leading AI tools. ### AI 2 — Gen AI & Cloud Foundations - **Units:** 3 - **Hours:** 48-54 Lecture - **Prerequisite:** Completion with a C or better in: AI 1 - **Transfers to:** CSU - **Offered:** Spring 2027, 1 section listed as AI-2 (/c/chaffey-college/schedule/spring-2027/ai.md) - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This Generative AI Foundations course offers an introduction to artificial intelligence with an emphasis on generative AI technologies and hands-on labs using the AWS cloud computing platform. The course will provide a broad foundational understanding of AI and Machine Learning (ML). The curriculum delves deeper into the concepts of generative AI and its applications in cloud computing. The concepts in this course are aligned to the generative AI concepts covered in AWS Certified AI Practitioner (AIF-C01) exam and will provide an overall understanding of cloud computing concepts, independent of specific technical roles. It provides a detailed overview of cloud concepts, AWS core services, security, architecture, pricing, and support. ### AI 3 — Ethics in AI and Technology - **Units:** 3 - **Hours:** 48-54 Lecture - **Transfers to:** CSU - **Offered:** Fall 2026, 1 section listed as AI-3 (/c/chaffey-college/schedule/fall-2026/ai.md); Spring 2027, 1 section listed as AI-3 (/c/chaffey-college/schedule/spring-2027/ai.md) - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course explores the ethical, social, and philosophical dimensions of artificial intelligence and emerging technologies. Students will examine how technologies such as machine learning, automation, robotics, data analytics, and generative AI impact privacy, employment, bias, surveillance, creativity, and human rights. Through case studies, debates, and critical analysis, the course challenges students to evaluate the moral responsibilities of technologists, policymakers, and organizations in designing and deploying AI systems. Topics include algorithmic fairness, data ethics, accountability and transparency, autonomous decision-making, intellectual property, and the future of human–machine relationships. ### AI 4 — Machine Learning Foundations - **Units:** 3 - **Hours:** 48-54 Lecture - **Prerequisite:** Completion with a C or better in: AI 2 - **Transfers to:** CSU - **Offered:** not on the Summer 2026, Fall 2026, Spring 2027 schedules - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course provides an applied introduction to Natural Language Processing (NLP) and its integration with machine learning and cloud-based AI services. Students learn how to process and extract text from diverse sources, apply the machine learning pipeline to NLP problems, and implement solutions for sentiment analysis, information extraction, and topic modeling using Amazon Comprehend and other AWS services. The course also introduces multilingual NLP through speech transcription and translation tools. Students explore the foundations of generative AI and large language models (LLMs), identify practical use cases, and learn to integrate LLMs into applications using AWS generative AI technologies. Hands-on labs and a capstone project give students experience designing, building, and evaluating real-world NLP solutions in the cloud. ### AI 5 — Certified Machine Learning Engineer Associate - **Units:** 3 - **Hours:** 48-54 Lecture - **Prerequisite:** Completion with a C or better in: AI 4 - **Transfers to:** CSU - **Offered:** not on the Summer 2026, Fall 2026, Spring 2027 schedules - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course prepares students for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) through a comprehensive, hands-on exploration of how to build, train, optimize, and deploy machine learning solutions on Amazon Web Services. Students will apply core ML concepts using AWS tools and services including SageMaker, Glue, Lambda, and CloudWatch while working with the data, compute, and storage resources essential for modern machine learning pipelines. ### AI 6 — Accelerated Computing and CUDA Architecture - **Units:** 3 - **Hours:** 48-54 Lecture - **Prerequisite:** Completion with a C or better in: AI 5 - **Transfers to:** CSU - **Offered:** not on the Summer 2026, Fall 2026, Spring 2027 schedules - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course provides an in-depth exploration of accelerated computing, CUDA architecture, and cloud-based AI engineering using Microsoft Azure’s suite of tools. Students learn how GPU acceleration and parallel programming enable the large-scale training and deployment of modern artificial intelligence and data-intensive systems. Through lectures, labs, and design projects, students will use CUDA programming, Azure AI Studio, Azure Machine Learning, Azure AI Services, and Azure AI Search to build, optimize, and deploy intelligent systems that integrate high-performance computing with cloud AI workflows. ### AI 7 — AI in Security & IT - **Units:** 3 - **Hours:** 48-54 Lecture - **Prerequisite:** Completion with a C or better in: AI 5 - **Transfers to:** CSU - **Offered:** not on the Summer 2026, Fall 2026, Spring 2027 schedules - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course explores the integration of artificial intelligence (AI) and machine learning (ML) in modern information technology and cybersecurity environments. Students will examine how AI-driven systems are transforming network defense, threat detection, risk management, and digital forensics. Emphasis is placed on understanding both the opportunities and challenges AI introduces to IT security — including automation, anomaly detection, adversarial attacks, data privacy, and ethical considerations.Through hands-on labs, case studies, and technical demonstrations, students will learn how to apply AI techniques to identify vulnerabilities, predict threats, and enhance security operations. The course also addresses governance frameworks, regulatory standards, and the responsible use of AI in critical infrastructure and enterprise settings. ### AI 8 — Deep Learning Engineering and Design - **Units:** 3 - **Hours:** 48-54 Lecture - **Prerequisite:** Completion with a C or better in: AI 5 - **Transfers to:** CSU - **Offered:** not on the Summer 2026, Fall 2026, Spring 2027 schedules - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course provides a comprehensive introduction to the principles, mathematics, and implementation techniques that underpin modern deep learning. Students gain a solid foundation in core concepts such as gradient descent, back propagation, computation graphs, and optimization of highly parameterized models. The course examines the essential building blocks of neural networks including linear, convolutional, and pooling layers, as well as common activation functions. The course introduces widely used architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Through hands-on labs and real-world applications in vision, language, and generative modeling, students learn to design, implement, train, and evaluate advanced deep learning systems. ### AI 9 — Natural Language Processing Engineering & Design - **Units:** 3 - **Hours:** 48-54 Lecture - **Prerequisite:** Completion with a C or better in: AI 5 and; COMPSCI 5 - **Transfers to:** CSU - **Offered:** not on the Summer 2026, Fall 2026, Spring 2027 schedules - Grading: Letter Grade - Credit for Prior Learning: Yes - TOP Code: 079900 This course introduces students to the engineering principles and design methodologies that power Natural Language Processing (NLP) systems. Through a blend of theory, coding labs, and applied projects, students explore how computers understand, interpret, and generate human language. Topics include linguistic foundations, text processing pipelines, tokenization, embeddings, transformers, sentiment analysis, conversational AI, and ethical considerations in language technologies. Students will gain hands-on experience using modern NLP frameworks such as spaCy, Hugging Face Transformers, and OpenAI APIs to design applications for real-world use cases—ranging from chatbots and translation systems to sentiment-aware interfaces and knowledge assistants. The course emphasizes both engineering rigor and creative design, encouraging students to prototype innovative, human-centered NLP systems that address social, cultural, and accessibility needs. By the end of the course, students will understand the computational models behind language, be able to design and implement NLP solutions, and critically assess the societal impacts of these technologies.