Course Syllabus

Summer 2026 | CIS-2380-VO99 - Introduction to Machine Learning

Independent Study Class

Faculty

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

Note

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.

Courses may only be used to meet one General Education Requirement.

Course Description

This course introduces concepts, terms, and applications of machine learning and artificial intelligence (AI). Topics include Python applications, data acquisition, supervised and unsupervised learning, and data modeling. Students learn how to build basic machine learning and AI projects and explore the current impact, issues, and future trends of AI and machine learning. Recommended prior or concurrent learning: Introduction to Artificial Intelligence, Introduction to Computer Science, and/or Python Programming.

Essential Objectives

1. Discuss the history and development of AI and machine learning including applications and use in various industries, sectors, and career fields.
2. Describe the differences between artificial intelligence (AI), machine learning, and deep learning.
3. Investigate and discuss the ethical issues and potential biases in machine learning applications.
4. Define and interpret the basic tools required for building machine learning projects.
5. Evaluate appropriate data preprocessing techniques to prepare data for machine learning models.
6. Develop and create a simple dashboard for visualizing data through Tableau.
7. Compare and classify different machine learning models available in supervised learning, unsupervised learning, and reinforcement learning.
8. Describe common terms and concepts used in the different steps of the AI project cycle.
9. Identify the functions of and common applications for neural networks and describe how they are different from biological neurons.
10. Develop basic Python-based use cases and AI projects that incorporate different machine learning models.
11. Examine different evaluation metrics to assess the performance of machine learning models.
12. Discuss and evaluate the future of machine learning based on current and emerging 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.

Artificial Intelligence(AI) Policy Statement

CCV recognizes that artificial intelligence (AI) and generative AI tools are widely available and becoming embedded in many online writing and creative applications.

Integrated: This course's generative AI policy acknowledges the use of AI is an essential skill in today's world. By using genAI for specific purposes, students become equipped with relevant skills and tools necessary to thrive in a technology-driven society. Emphasizing the mastery of generative AI should empower you to harness its potential, enhancing your problem-solving abilities and preparing you for future challenges and opportunities. Be aware, however, that any time generative AI is used at any point in the assignment without attribution it may be considered a violation of CCV's Academic Integrity Policy.

Artificial intelligence (AI) tools are allowed in this course as learning aids, similar to tutoring, documentation, or office hours. You may use AI to help search for sources, explore unfamiliar topics, clarify concepts, debug code you wrote, or improve the clarity and mechanics of your own writing. Used this way, AI can support your learning without replacing it.

However, AI may not be used to do the core thinking for you. You may not submit AI-generated answers, essays, code, or solutions as your own, nor use AI to complete assignments in place of your own reasoning. If the AI is doing the thinking for you, that use is not permitted. When AI contributes meaningfully to your work, you are expected to disclose that use. When in doubt about whether a particular use is appropriate, ask before submitting.

Some assignments in this course may explicitly prohibit, limit, or require the use of AI tools. Always follow the instructions provided for each assignment, even when they differ from the general course policy. When AI tools are used in a way that meaningfully supports your work, you must acknowledge that use. Disclosure is not a penalty. It is part of academic honesty and professional practice.

Appropriate examples of AI use:

• Using AI to suggest search terms or help locate scholarly sources
• Asking AI to check your work or explain a concept after attempting the problem yourself
• Debugging or reviewing code that you wrote
• Checking grammar or improving clarity of your own writing

Inappropriate examples of AI use:

• Submitting AI-generated essays, discussion posts, or lab answers
• Using AI to solve problems or design solutions without understanding them
• Paraphrasing AI output and presenting it as your own work
• Relying on AI-generated code or explanations you cannot explain

Example AI disclosure and ctiation:

AI tools used: Google Gemeni
Purpose: Helped identify sources and clarify concepts related to cloud scalability.

Google. (2025). Gemini (large language model) [Artificial intelligence software]. https://gemini.google.com

Methods

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

Evaluation Criteria

• Assignments: 28%
• Quizzes: 22%
• Discussions: 30%
• Labs: 20%

Grading Criteria

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

	High	Low
A+	100	98
A	Less than 98	93
A-	Less than 93	90
B+	Less than 90	88
B	Less than 88	83
B-	Less than 83	80
C+	Less than 80	78
C	Less than 78	73
C-	Less than 73	70
D+	Less than 70	68
D	Less than 68	63
D-	Less than 63	60
F	Less than 60
P	100	60
NP	Less than 60	0

Weekly Schedule

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

Late assignments are permitted with a 10% per day late penalty, unless otherwise coordinated with the instructor.

Accessibility Services for Students with Disabilities:

1. Provide disability documentation to the Accessibility Coordinator at your academic center. https://ccv.edu/student-support/accessibility-services/
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

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