Artificial Intelligence
Overview
Folsom Lake College's artificial intelligence (AI) department fosters innovation, meets industry demands, and advances education in a rapidly evolving field. It provides a structured platform for AI. By offering a range of courses, from foundational subjects to advanced topics, the department prepares students with the skills needed to tackle real-world AI challenges. It also promotes interdisciplinary collaboration, ensuring AI professionals understand their work's ethical implications and societal impacts.
Certificates Offered
- Artificial Intelligence and Machine Learning Certificate
- Division Dean Dr. Lorena Navarro
-
Department Chair
Dr. Suha Al Juboori
- Phone (916) 608-6615
- Email navarrl@flc.losrios.edu
Certificate of Achievement
Artificial Intelligence and Machine Learning Certificate
Artificial Intelligence and Machine Learning certificate focuses on building machine learning models that can be used for predicting, making decisions and enhancing human capabilities. The program provides opportunities to develop the necessary skills and basic aptitudes in Artificial Intelligence and Machine Learning that is required in different fields including the information technology, automotive, healthcare, aerospace, industrial, and manufacturing industries.
Catalog Date: August 1, 2026
Certificate Requirements
| Course Code | Course Title | Units |
|---|---|---|
| AI 300 | Introduction to Artificial Intelligence and Machine Learning | 3 |
| AI 310 | Machine Learning | 3 |
| AI 305 | Ethics and Artificial Intelligence | 3 |
| AI 311 | Python for Applied AI and Visualization (4) | 4 |
| or CISP 407 | Programming in Python (4) | |
| AI 312 | Natural Language Processing I (3) | 3 |
| or AI 314 | Computer Vision I (3) | |
| Total Units: | 16 |
Student Learning Outcomes
Upon completion of this program, the student will be able to:
- explain how artificial intelligence and machine learning is useful in business or career.
- apply common artificial intelligence (AI) concepts and methodologies.
- utilize methods of machine learning and deep learning to build and run analytical models.
- explain how to use existing artificial intelligence and machine learning programming libraries on a data set to create a valid model that justifies their design decisions.
Career Information
Artificial intelligence programmer, machine learning engineer, data scientist, and business intelligence developer are possible job opportunities. The program provides the industry professional with the knowledge and skills used in a variety of fields using artificial intelligence.
Artificial Intelligence (AI) Courses
AI 299 Experimental Offering in Artificial Intelligence
- Units:0.5 - 4
- Prerequisite:None.
- Catalog Date:August 1, 2026
This is the experimental courses description.
AI 300 Introduction to Artificial Intelligence and Machine Learning
- Units:3
- Hours:54 hours LEC
- Prerequisite:None.
- Transferable:CSU; UC
- Catalog Date:August 1, 2026
This course introduces students to artificial intelligence (AI) and machine learning (ML) basics. It explores AI use cases and applications and explains AI concepts and terms like generative AI (GenAI), deep learning (DL), computer vision, and natural language processing (NLP). Students will also be exposed to various issues and concerns surrounding AI, such as ethics and bias. This course does not require programming. This course is not open to those who have completed CISD 300.
AI 305 Ethics and Artificial Intelligence
- Units:3
- Hours:54 hours LEC
- Prerequisite:None.
- Transferable:CSU; UC
- Catalog Date:August 1, 2026
This introductory course on Artificial Intelligence (AI) ethics provides a comprehensive overview of ethical considerations in the domain of artificial intelligence. The course covers principles of AI ethics, strategies to foster fair and equitable AI systems, approaches to minimize biases, and methods to address key issues and establish user trust.
AI 310 Machine Learning
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 300 with a grade of "C" or better
- Transferable:CSU; UC
- Catalog Date:August 1, 2026
This course introduces Machine Learning (ML) and Deep Learning (DL), focusing on their differences, mathematical foundations, and practical applications. Students will build classification, regression, and reinforcement learning models while exploring AI project structuring and emerging technologies. This course is not open to those who have completed CISD 307.
AI 311 Python for Applied AI and Visualization
- Units:4
- Hours:54 hours LEC; 54 hours LAB
- Prerequisite:None.
- Transferable:CSU (effective Fall 2026)
- General Education:Local GE L2 (effective Fall 2026)
- Catalog Date:August 1, 2026
This course equips students with the foundational concepts and practice of Python programming, AI/ML tools and techniques, and visualization: Data, variables and structures, functions, AI datasets, arrays, lists, tuples, and objects; AI/ML algorithms and programming process; visualization and performance analysis for applications using current libraries/packages with NumPy, Pandas, Scikit-learn, TensorFlow, Keras, Matplotlib, and Seaborn.
AI 312 Natural Language Processing I
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 300 with a grade of "C" or better
- Transferable:CSU; UC
- Catalog Date:August 1, 2026
This course introduces students to the basics of Natural Language Processing (NLP) and how to give the ability of a computer program to understand human language as it is spoken and written, referred to as natural language. It is a component of artificial intelligence (AI). This course is not open to those who have completed CISD 410.
AI 314 Computer Vision I
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 300 with a grade of "C" or better
- Transferable:CSU; UC
- Catalog Date:August 1, 2026
This course introduces students to the basics of Computer Vision (CV) which is a subset of Artificial Intelligence that train computers to automatically process, extract and manipulate visual data from images and videos. This course is not open to those who have completed CISD 412.
AI 315 Deep Learning I
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 300 with a grade of "C" or better
- Transferable:CSU (effective Fall 2026)
- Catalog Date:August 1, 2026
This course provides students with the fundamental concepts of Deep Learning (DL) as a subset of Machine Learning (ML), including basic practice of DL and its applications, multi-layer neural network architectures in DL models, propagation algorithms, parameters, and collections of data, and DL tools/libraries/packages.
AI 316 Applied Generative Artificial Intelligence I
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 300 with a grade of "C" or better
- Transferable:CSU (effective Fall 2026)
- Catalog Date:August 1, 2026
This course introduces students to the fundamental concepts of Applied Generative Artificial Intelligence (GenAI) as part of AI technologies and application development. It explores basic practices of GenAI and its applications, large language models (LLMs), transformer architecture, retrieval-augmented generation (RAG), the world of graphics processing units (GPUs) and neural processing units (NPUs), and hands-on GenAI application development.
AI 400 Applied Generative Artificial Intelligence II
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 316 with a grade of "C" or better
- Transferable:CSU (effective Fall 2026)
- Catalog Date:August 1, 2026
This Advanced Generative Artificial Intelligence (GenAI) applications course will introduce Advanced GenAI applications in areas such as Healthcare and Autonomous Systems, forefront technologies and advanced techniques in testing and building combined GenAI and Retrieval-Augmented Generation (RAG) applications through complex and multi-language and data models, and applying advanced prompt engineering and concepts of federated learning (FL) in application development.
AI 402 Deep Learning II
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 315 with a grade of "C" or better
- Catalog Date:August 1, 2026
This Advanced Deep Learning (DL) course will provide forefront technologies and techniques for testing and building applications using complex and multi-layered neural networks (NNs), generative adversarial networks (GANs), variational autoencoders (VAEs), deep reinforcement learning (DRL), and other transformers and unsupervised models.
AI 404 Computer Vision II
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 314 with a grade of "C" or better
- Transferable:CSU (effective Fall 2026)
- Catalog Date:August 1, 2026
This course is a continuation of Computer Vision I and provides an in-depth study of advanced computer vision techniques and modern deep learning-based approaches. Students will explore convolutional neural networks (CNNs), object detection and segmentation architectures, generative models, 3D vision, and real-world deployment of vision systems. Emphasis is placed on hands-on implementation using industry-standard frameworks such as PyTorch and OpenCV. Students will complete a significant capstone project integrating multiple computer vision techniques.
AI 406 Natural Language Processing II
- Units:3
- Hours:54 hours LEC
- Prerequisite:AI 312 with a grade of "C" or better
- Transferable:CSU (effective Fall 2026)
- Catalog Date:August 1, 2026
This course builds on foundational NLP concepts by focusing on advanced techniques used in modern AI systems. Students evaluate, fine-tune, and optimize large language models (LLMs) such as GPT-4 and LLaMA for real-world applications. Topics include Retrieval-Augmented Generation (RAG), advanced prompt engineering, instruction tuning, and reinforcement learning from human feedback (RLHF). Students design and deploy production-ready NLP systems using APIs and cloud platforms, and develop multi-modal applications that integrate text with other data types. Emphasis is on hands-on, real-world implementation.
AI 499 Experimental Offering in Artificial Intelligence
- Units:0.5 - 4
- Prerequisite:None.
- Catalog Date:August 1, 2026
This is the experimental courses description.