Untitled

APPLY NOW

Web Schedules

Fall 2025
Spring 2026
Summer 2026

One Credit Courses

Fall 2025
Spring 2026
Summer 2026

No Cost Textbook/Resources Courses

Fall 2025
Spring 2026
Summer 2026

Low Cost Textbook/Resources Courses

Fall 2025
Spring 2026
Summer 2026

Course Planning by Program

2025-26

Essential Objectives

Course Syllabus


Revision Date: 30-Aug-25
 

Fall 2025 | CIS-1270-VO02 - Introduction to Artificial Intelligence (AI)


Online Class

Online courses take place 100% online via Canvas, without required in-person or Zoom meetings.

Location: Online
Credits: 3 (45 hours)
Day/Times: Meets online
Semester Dates: 09-02-2025 to 12-15-2025
Last day to add this section: 09-11-2025
Last day to drop without a grade: 09-15-2025 - Refund Policy
Last day to withdraw (W grade): 11-04-2025 - Refund Policy
This course has started, please contact the offering academic center about registration

Faculty

Tyler Whitney
View Faculty Credentials
View Faculty Statement
Hiring Coordinator for this course: Deb Grant

General Education Requirements


This section meets the following CCV General Education Requirement(s) for the current catalog year:
Digital and Computing Literacy
    Note
  1. Many degree programs have specific general education recommendations. In order to avoid taking unnecessary classes, please consult with additional resources like your program evaluation, your academic program catalog year page, and your academic advisor.
  2. Courses may only be used to meet one General Education Requirement.

Course Description

This course introduces basic concepts and applications of artificial intelligence (AI), including AI project cycles and tools. Students explore and identify issues surrounding AI including ethics, bias, culture, regulations, and professional expectations, as well as the growing impact of AI on different fields and industries. Prior learning in computer science is not required.


Essential Objectives

1. Identify AI and differentiate between automation and AI.
2. Discuss and explain the history, origins, and development of AI including current trends and applications in various industry sectors and career fields.
3. Demonstrate an understanding of how different AI applications and datasets are used in various settings.
4. Identify the different stages of an AI project required to follow industry standards and examine the different roles each member of a typical data science team plays in a project.
5. Examine the cultural and social impacts, ethical concerns, and issues of bias and regulation around AI practices now and in the future.
6. Describe basic concepts and models encountered in machine learning and deep learning.
7. Classify different kinds of data, evaluate the quality of data, and examine different methods of data mining and storage.
8. Examine common no-code tools available for AI project building and develop a use case using no-code tools in each domain of AI.
9. Discuss the future of AI based on emerging technological trends.


Required Technology

More information on general computer and internet recommendations is available on the CCV computer recommendations Support page.

Please see CCV's Digital Equity Statement (pg. 45) to learn more about CCV's commitment to supporting all students access the technology they need to successfully finish their courses.


Required Textbooks and Resources

This course only uses free Open Educational Resources (OER) and/or library materials. For details, see the Canvas Site for this class.


Methods

  • online forum discussions
  • interactive labs
  • multimedia presentations and resources
  • readings, writing, and inquiry-based research

Evaluation Criteria

  • Assignments: 25%
  • Quizzes: 25%
  • Discussions: 25%
  • Projects: 20%
  • Self-Assessments: 5%

Grading Criteria

CCV Letter Grades as outlined in the Evaluation System Policy are assigned according to the following chart:

 HighLow
A+10098
A Less than 9893
A-Less than 9390
B+Less than 9088
B Less than 8883
B-Less than 8380
C+Less than 8078
C Less than 7873
C-Less than 7370
D+Less than 7068
D Less than 6863
D-Less than 6360
FLess than 60 
P10060
NPLess than 600


Weekly Schedule


Week/ModuleTopic  Readings  Assignments
 

1

Demystifying AI & the Evolution of AI

Students will be introduced to the emerging field of Artificial Intelligence and its potential applications. They will understand the difference between AI and Automation. In this week, the students will go through the history and evolution of AI starting with the integrated chips. They will also be introduced to current industry leaders in this field.

  

Required Readings & Resources

  
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

2

AI and Technology

In this session, students will explore the history of data collection and its relationship with AI. They will learn about the evolution of data collection methods and their use in powering AI applications. The session will also examine the limitations of AI, including its tendency towards bias and errors, and the ethical considerations involved in deploying AI systems along with synthetic data generation using Generative AI. Finally, students will explore the latest trends in AI and emerging technologies. They will learn about the impact of 5G networks, which are enabling faster and more reliable communication, and how this is fueling the growth of the Internet of Things (IoT).

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

3

Industry 4.0 Digitization

Students will learn what Digitalization and its importance is. They will be introduced to AI in industry, its various applications, especially in the fields of Manufacturing, Healthcare, Transportation, Agriculture, and Energy and to Leveraging Generative AI in Various Industries.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

4

Domains of AI

In this lesson, students will be introduced to the different domains of AI, including machine learning, deep learning,natural language processing, and computer vision. They will understand the unique characteristics and potential applications of each domain. Additionally, they will learn about the importance of datasets and statistical analysis in AI.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

5

AI Project Cycle Part 1

The different stages of the AI Project will be introduced to students. Students will take a deep dive into the stages chronologically, starting with Problem Scoping, Data Acquisition, and Data Exploration.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

6

AI Project Cycle Part 2

In the AI Project Cycle, students will learn about the various stages of an AI project, including modeling, evaluation, and deployment. They will learn about the different types of AI models, such as linear regression, deep neural networks, and decision trees, and how to evaluate their performance using methods like confusion matrices, accuracy, and F1 score. Students will also learn about the key considerations for deploying AI models in production, such as hardware requirements and processing power.

Additionally, students will learn the ethical considerations that need to be taken while working with AI and the different societal impacts that AI has. They will learn to make AI inclusive and eliminate bias.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

7

Societal Impacts of AI

Students will learn the ethical considerations that need to be taken while working with AI and the different societal impacts that AI has. They will learn to make AI inclusive and strive to eliminate bias.

  
  • Module 7 in Canvas
  
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

8

Elements of Machine Learning (ML)

By the end of this lesson, students will understand the basics of machine learning, including its definition, history, how it works, and its various models, and be able to differentiate between supervised, unsupervised, and reinforcement learning.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

9

Elements of Deep Learning (DL)

In this session, students will be introduced to Deep Learning. They will learn what neural networks are and what was the inspiration behind developing neural networks. They will be taught some common Deep Learning models and applications.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment

 

10

Data - Fuel for AI

Students will learn about the importance of data literacy in today's digital world. They will understand the differences between structured data and unstructured data and the challenges associated with handling each type of data. Additionally, students will be introduced to various data mining methods, such as clustering, classification, and association rule learning, and the significance of each method in analyzing large datasets along with Generative Models in Data Mining. Finally, the session will cover different methods of data storage, including relational databases, NOSQL databases, and data warehouses, and their respective advantages and disadvantages.

  
  • Module 10 in Canvas

Section 1: What is Data literacy?

Section 2: Structured Data and Unstructured Data

Section 4: Big Data

  
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

11

AI Data Science Teams

In this session, students will learn about the motivation behind a data science team; the different AI/Data science roles available; the key considerations in AI/Data Science Project Management and the salient features of a data science team along with Generative AI Techniques in various roles. Students will be able to describe the different roles while working on an AI project and discuss the roles played by different key stakeholders in a typical AI team.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

12

Tools to Implement AI

In this lesson, students will learn about the various tools available for implementing AI, including both no-code and code-based options. They will explore thelandscape of no-code AI tools, as well as gain an understanding of the traditional AI process and how it compares to the no-code approach. Additionally, students will be introduced to Teachable Machine, a no-code tool for creating AI models.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment

 

13

AI Project I – Statistical Data

Students will be introduced to the various no-code tools for Statistical Data. They will be introduced to Orange Data Mining and given a step by step approach to install, use, and perform tasks using Orange.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

14

AI Project II – Natural Language Processing

At the end of the lesson, students will be able to understand the concept of Natural Language Processing, differentiate between AI and not-AI tasks, distinguish between automation and AI, and describe the history of AI and its origin. They also learn about the different components of NLP and the applications of NLP in the real world. Additionally, they explore the features of Chatfuel and understand how to implement a no-code NLP solution.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment

 

15

AI Project II – Natural Language Processing

This lesson covers various topics related to the future of AI, including Reinforcement Learning along with Reinforcement Learning with Human Feedback (RLHF) in Generative AI, Quantum Computing, Artificial General Intelligence, and emerging software and hardware technologies. Students will learn about the differences between these technologies and their potential applications, challenges, and critical milestones. They will also explore the impact of hardware and software on AI and its capabilities along with Hardware Acceleration for Generative AI. By the end of the lesson, students will have a comprehensive understanding of the latest developments in the field of AI and their implications for the future.

     
  • Discussion
  • Assignemnt
  • Quiz
  • Self Assessment
 

Attendance Policy

Regular attendance and participation in classes are essential for success in and are completion requirements for courses at CCV. A student's failure to meet attendance requirements as specified in course descriptions will normally result in a non-satisfactory grade.

  • In general, missing more than 20% of a course due to absences, lateness or early departures may jeopardize a student's ability to earn a satisfactory final grade.
  • Attending an on-ground or synchronous course means a student appeared in the live classroom for at least a meaningful portion of a given class meeting. Attending an online course means a student posted a discussion forum response, completed a quiz or attempted some other academically required activity. Simply viewing a course item or module does not count as attendance.
  • Meeting the minimum attendance requirement for a course does not mean a student has satisfied the academic requirements for participation, which require students to go above and beyond simply attending a portion of the class. Faculty members will individually determine what constitutes participation in each course they teach and explain in their course descriptions how participation factors into a student's final grade.


Participation Expectations

Students are expected to complete the assignments each week. Posts in the discussion forums should be created mid week to give a chance for fellow students to respond. At least one response to a fellow student is expected for full discussion credit.



Missing & Late Work Policy

The late policy for this course is a 10% penalty on assignments per day late, unless extenuating circumstances or contact ahead of time to negotiate an extension.


Accessibility Services for Students with Disabilities:


CCV strives to mitigate barriers to course access for students with documented disabilities. To request accommodations, please
  1. Provide disability documentation to the Accessibility Coordinator at your academic center. https://ccv.edu/discover-resources/students-with-disabilities/
  2. Request an appointment to meet with accessibility coordinator to discuss your request and create an accommodation plan.
  3. Once created, students will share the accommodation plan with faculty. Please note, faculty cannot make disability accommodations outside of this process.


Academic Integrity


CCV has a commitment to honesty and excellence in academic work and expects the same from all students. Academic dishonesty, or cheating, can occur whenever you present -as your own work- something that you did not do. You can also be guilty of cheating if you help someone else cheat. Being unaware of what constitutes academic dishonesty (such as knowing what plagiarism is) does not absolve a student of the responsibility to be honest in his/her academic work. Academic dishonesty is taken very seriously and may lead to dismissal from the College.

Apply Now for this semester.

Register for this semester: March 31 - August 29, 2025