ICS 352 Machine Learning
This course presents the key algorithms and theory of machine learning. Students will examine supervised and unsupervised learning algorithms. And they will gain an understanding of machine learning foundational concepts used in artificial intelligence, statistics and data science. Topics include learning algorithms used in recent application as autonomous vehicles, google search, and Facebook photo tags.
First day attendance is mandatory.
Note: Students are responsible to both be aware of and abide by prerequisites for ICS courses for which they enroll, and will be administratively dropped from a course if they have not met prerequisites.
Prerequisites
Special information
Note: Students are responsible to both be aware of and abide by prerequisites for ICS courses for which they enroll, and will be administratively dropped from a course if they have not met prerequisites.
4 Undergraduate credits
Effective May 3, 2018 to present
Learning outcomes
General
- Distinguish the basic theory used in machine learning.
- Study and examine different learning approaches such as decision tree learning, Bayesian learning and artificial neural networks.
- Implement and test different types of learning algorithms.
- Formulate machine learning problems corresponding to different applications
- Describe the application of learning algorithms to a wide range of data
Spring 2024
| Section | Title | Instructor | books | eservices |
|---|---|---|---|---|
| 50 | Machine Learning | Sulieman, Dalia | Books for ICS-352-50 Spring 2024 | Course details for ICS-352-50 Spring 2024 |
Fall 2024
| Section | Title | Instructor | books | eservices |
|---|---|---|---|---|
| 50 | Machine Learning | Munmun, Mousumi | Books for ICS-352-50 Fall 2024 | Course details for ICS-352-50 Fall 2024 |