Computer Science and Information Systems Department

Cutting-edge AI concepts, machine learning techniques, and real-world applications
  • 10 specialized courses: foundational courses, machine learning courses, applied AI courses
  • 4 credential programs: from non-credit, no cost certificates to an associate in science (AS) degree
  • No prerequisite to start
  • Includes UC/CSU transferable courses

Applied AI Programs

Our suite of Applied AI programs offers a clear pathway for learners at every level.

  • Those looking for a foundational academic footing can begin with the Certificate of Achievement in Applied AI, which introduces core AI methodologies, Python programming, prompt engineering, and responsible AI, all with no heavy math or programming background required.
     
    Students ready to go further can pursue the Certificate of Achievement – Advanced in Applied AI, which dives into machine learning, deep learning frameworks, and natural language processing, culminating in a hands-on capstone project. 

    Finally, students seeking a full degree can work toward the Associate in Science (AS) in Applied AI degree, which builds on the Advanced Certificate and on general education requirements across communication, sciences, and the humanities.

  • For those who prefer a non-credit, no cost entry point into AI, the Certificate of Completion in Applied AI covers AI fundamentals, data management, and human-AI collaboration, all without requiring math or a technical background.
        
  • For working professionals seeking practical, immediately applicable skills, the Certificate of Completion in Applying AI at Work focuses on strategic AI use in the workplace: sharpening judgment, critical thinking, and synthesis skills at a flexible, self-paced schedule.

Certificate of Achievement in Applied AI

Courses in the certificate:

CIS 7 - Introduction to Artificial Intelligence

  • Prerequisite: None
  • UC/CSU transferable

Course Objectives

  1. Define Artificial Intelligence
  2. Describe Modern AI Principles
  3. Describe Machine Learning Models
  4. Describe AI Models
  5. Apply Generative AI Systems
  6. Discuss Hardware for AI
  7. Evaluate Ethics in AI

CIS 51 - Introduction to Prompt Engineering and AI Agents

  • Prerequisite: None
  • Advisory: CIS 4 - Computer Literacy
  • CSU transferable

Course Objectives:

  1. Creating effective Large Language Model (LLM) Prompts
  2. Understand and use important Prompt Patterns
  3. Apply prompt knowledge to a wide range of application types
  4. Understand the advantages of Retrieval Augmented Generation (RAG) and use it successfully
  5. Construct a simple AI Agent
  6. Understand the advantages of Customized GPTs and use them successfully
  7. Appreciate and assess the risks of prompting an AI

CIS 67 - Implementing Responsible AI

  • Prerequisite: None
  • CSU transferable

Course Objectives:

  1. Understand Ethical and Governance Principles for AI
  2. Implement Responsible AI in Network and Infrastructure Layers
  3. Develop AI-Driven Solutions While Ensuring Operating System (OS) Security
  4. Ensure Ethical Use of AI in Databases and Data Management
  5. Deploy AI Responsibly in Virtual Machines and Cloud Infrastructure
  6. Integrate Responsible AI in Programming Languages and Development Frameworks
  7. Design Ethical AI for Mobile and Web Applications
  8. Enhance End-User Trust Through AI Transparency and Explainability
  9. Promote Inclusive and Fair AI-Driven User Experience (UX) Design

CIS 40 - Introduction to Programming in Python

  • Prerequisite: None
  • UC/CSU transferable
Catalog description

Certificate of Achievement - Advanced in Applied AI

Courses in the certificate:

CIS 17A - Introduction to Machine Learning

  • Prerequisites:
    • CIS 7 - Introduction to AI and Stat 1000 - Introduction to Statistics
    • Or
    • CIS 11/111X - Foundations of Data Science
  • Advisory: CIS 40 - Introduction to Programming in Python
  • UC/CSU transferable

Course Objectives

  1. Define Artificial Intelligence
  2. Describe Modern AI Principles
  3. Describe Machine Learning Models
  4. Describe AI Models
  5. Apply Generative AI Systems
  6. Discuss Hardware for AI
  7. Evaluate Ethics in AI

CIS 17B - Foundations of Machine Learning

  • Prerequisites:
    • CIS 17A - Introduction to Machine Learning
    • MATH 1C - Calculus III
  • UC/CSU transferable

Course Objectives:

  1. Apply Linear Algebra and Calculus in Machine Learning Optimization
  2. Investigate Statistical Learning
  3. Examine Statistical Learning in Regression Models
  4. Examine Statistical Learning in Classification Models
  5. Examine Statistical Learning in Decision Tree Models
  6. Examine Statistical Learning in Support Vector Machines
  7. Examine Statistical Learning in Unsupervised Learning

Two selected CIS courses

  • CIS 22C - Data Abstraction and Structures
  • CIS 41A - Python Programming
  • CIS 44A - Database Management Systems
  • CIS 44F - Introduction to Big Data and Analytics
  • CIS 44H - R Programming
  • CIS 64B - Introduction to SQL
  • CIS 64G - Data Visualization Methodology and Tools
  • CIS 78 - Introduction to Deep Learning
  • CIS 80 - Introduction to Natural Language Processing

CIS 78 - Introduction to Deep Learning

  • Prerequisite:
    CIS 17B - Foundations of Machine Learning
  • CSU transferable

Course Objectives:

  1. Define and Describe the Foundational Concepts of Deep Learning
  2. Apply the Mathematical Principles Behind Deep Learning
  3. Implement Basic Neural Networks and Deep Learning Models
  4. Evaluate and Improve Deep Learning Models
  5. Utilize Deep Learning Frameworks and Libraries Effectively
  6. Explore Real-World Applications of Deep Learning

CIS 80 - Introduction to Natural Language Processing

  • Prerequisite:
    CIS 17B - Foundations of Machine Learning
  • CSU transferable

Course Objectives:

  1. Apply Linear Algebra and Calculus in Machine Learning Optimization
  2. Investigate Statistical Learning
  3. Examine Statistical Learning in Regression Models
  4. Examine Statistical Learning in Classification Models
  5. Examine Statistical Learning in Decision Tree Models
  6. Examine Statistical Learning in Support Vector Machines
  7. Examine Statistical Learning in Unsupervised Learning

CIS 82Y - Capstone Project

  • Prerequisite:
    CIS 17B - Foundations of Machine Learning

Associate of Science (AS) in Applied AI

Courses in the degree:

Certificate of Completion in Applied AI

Non-credit, no cost.

Courses in the certificate:

CIS 307 - Introduction to Artificial Intelligence

  • Prerequisite: None
  • UC/CSU transferable
  • Non-credit option

Course Objectives

  1. Define Artificial Intelligence
  2. Describe Modern AI Principles
  3. Describe Machine Learning Models
  4. Describe AI Models
  5. Apply Generative AI Systems
  6. Discuss Hardware for AI
  7. Evaluate Ethics in AI

CIS 311 - Foundations of Data Science for All

  • Prerequisite: None
  • UC/CSU Transferable

Catalog description

CIS 311X - Support for Foundations of Data Science for All

  • Prerequisite: None
  • Support course for CIS 311 - Foundations of Data Science for All

Catalog description

CIS 351 - Introduction to Prompt Engineering and AI Agents

  • Prerequisite: None
  • Advisory: CIS 4 - Computer Literacy
  • CSU transferable
  • Non-credit option

Course Objectives:

  1. Creating effective Large Language Model (LLM) Prompts
  2. Understand and use important Prompt Patterns
  3. Apply prompt knowledge to a wide range of application types
  4. Understand the advantages of Retrieval Augmented Generation (RAG) and use it successfully
  5. Construct a simple AI Agent
  6. Understand the advantages of Customized GPTs and use them successfully
  7. Appreciate and assess the risks of prompting an AI

CIS 367 - Implementing Responsible AI

  • Prerequisite: None
  • CSU transferable
  • Non-credit option

Course Objectives:

  1. Understand Ethical and Governance Principles for AI
  2. Implement Responsible AI in Network and Infrastructure Layers
  3. Develop AI-Driven Solutions While Ensuring Operating System (OS) Security
  4. Ensure Ethical Use of AI in Databases and Data Management
  5. Deploy AI Responsibly in Virtual Machines and Cloud Infrastructure
  6. Integrate Responsible AI in Programming Languages and Development Frameworks
  7. Design Ethical AI for Mobile and Web Applications
  8. Enhance End-User Trust Through AI Transparency and Explainability
  9. Promote Inclusive and Fair AI-Driven User Experience (UX) Design

Certificate of Completion in Applying AI at Work

Non-credit, no cost.

Courses in the certificate:

CIS 501 - Reframing Problems with AI

  • Prerequisite: None
  • Flexible schedule
  • For adult learners

Course Objectives:

A. Reframe problems.
B. Direct AI to explore problem solving goals.
C. Integrate stakeholder perspectives.
D. Conduct self-directed learning.
E. Synthesize and assess problem solving goals.

CIS 502 - Designing Solutions with AI

  • Prerequisite: None
  • Flexible schedule
  • For adult learner

Course Objectives:

A. Design a system level solution.
B. Validate assumptions before building.
C. Build and deliver solution.
D. Work collaboratively while maintaining individual accountability.
E. Reflect on and articulate human contribution.

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