DAT 105 - Fundamentals of Artificial Intelligence, Experimental
Credits: 3
3 Lecture Hours
Prerequisites: Suggested students at least qualify for MAT 108 and basic programming skills.
Description
This fundamentals course provides students with an overview of the different areas of artificial intelligence (AI). Students explore and practice with basic applications of machine learning theories and applications in different disciplines. Fundamentals of AI provides students with hands-on practice of basic machine learning focusing on different platforms used with machine learning and data set integration.
Learning Outcomes
Upon successful completion of the course, the student will:
- Articulate the differences between artificial intelligence (AI) and machine learning (ML).
- Discuss the differences between supervised and unsupervised learning.
- Examine the application of machine learning in interdisciplinary environments.
- Analyze different industry standard frameworks used with machine learning.
- Determine hardware options to create learning algorithms.
- Develop basic projects utilizing machine learning libraries.
Listed Topics
- Artificial intelligence (AI)
- Machine learning (ML)
- Supervised learning
- Unsupervised learning
- Harware options and uses
- Computer languages and machine mearning
- Data sets in machine learning
- Machine learning platforms and libraries
Reference Materials
Instructor approved materials and textbooks.
Students who successfully complete this course acquire general knowledge, skills and abilities that align with CCAC’s definition of an educated person. Specifically, this course fulfills these General Education Goals: - Critical Thinking and Problem Solving
- Technological Competence
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