DAT 280 - Fundamentals of Machine Learning
Credits: 3
3 Skills Lab Hours
Prerequisites: DAT 129 and DAT 202
Description
This course provides students with an overview of the basic application of machine learning in data analytics. Students explore and practice with basic applications of machine learning theories and applications in different disciplines. Fundamentals of Machine Learning provides students with hands-on practice of basic machine learning focusing on different algorithms used with data sets.
Learning Outcomes
Upon successful completion of the course, the student will:
- Articulate uses of machine learning (ML) in different industries.
- 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.
- Evaluate performance of various models on data.
- Develop basic projects utilizing machine learning algorithms.
Listed Topics
- Machine learning (ML)
- Artificial intelligence (AI)
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Computer languages and machine learning
- Datasets in machine learning
- Machine learning algorithms and implementation
Reference Materials
Instructor approved materials and textbooks, Laptop (not Chromebook) with operating system installed
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 & Problem Solving
- Technological Competence
Approved By: Dr. Quintin B. Bullock Date Approved: 12/14/2020
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