Course Syllabus

Spring 2025 | CIS-2710-VO01 - ST in CIS: Intro to Machine Learning

Online Class

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

Faculty

Tyler Whitney
View Faculty Credentials

Hiring Coordinator for this course: Deb Grant

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 difference between artificial intelligence (AI), machine learning, and deep learning, and how the latter are a subset of AI.
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. Explain how neural networks function and are different from biological neurons, and identify common applications.
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 IT Support page. https://support.ccv.edu/general/computer-recommendations/

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

Textbook Information will be posted here on December 6.

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

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.

Accessibility Services for Students with Disabilities:

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