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Apr 29, 2024
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ITAD 415 - Introduction to Machine Learning
5.0 Credits
The course takes an introductory look at machine learning, beginning with analyzing problems and creating appropriate tasks for training computing systems. Probability and similarities are utilized to aid in understanding and programming for the machine learning process. Artificial neural networks and how they are implemented to garner artificial intelligence are discussed. Additional topics include: decision trees, computational learning theory, and performance evaluation.
Prerequisite ITAD 360 and ITAD 375.
Course-level Learning Objectives (CLOs)
Upon successful completion of this course, students will be able to:
- Select the appropriate search problem to use for a machine learning task.
- Analyze the use of probability and similarities for machine learning.
- Examine artificial neural networks and how they mimic biological neural networks in order to assist machine learning from various inputs.
- Demonstrate knowledge of decision trees, the foundational mechanism that machine learning builds upon.
- Assess performance evaluation methodologies and measurement techniques implemented in machine learning.
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