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| Semester: | Fall 2024 |
| Number: | 0145-335-001 |
| Instructor: | Sukun Li |
| Days: | Tuesday Thursday 10:50 am - 12:05 pm |
| Note: | Traditional In-Person Class |
| Location: | Garden City - Swirbul Library 100 |
| Credits: | 3 |
| Course Materials: | View Text Books |
| Description: |
Students will explore machine learning principles and applications, including topics in: data types and preprocessing; data visualization; and supervised and unsupervised learning. Students will be able to: choose appropriate algorithms given specific problems; implement solutions that rely on machine learning; and articulate benefits and limitations of specific machine learning approaches. |
| Learning Goals: |
1) Students will articulate the KDD (Knowledge Discovery in Databases) process.2) Students will utilize a modern programming language to conduct exploratory data visualizations with test data sets.3) Students will examine supervised and unsupervised learning procedures.4) Students will apply basic algorithms from regression, clustering, classification, association analysis, and anomaly detection. Students will understand issues arising from data overfitting.5) Students will choose appropriate machine learning (ML) algorithms given specific problems; students will implement solutions that rely on ML; students will articulate benefits and limitations of specific ML approaches. *The learning goals displayed here are those for one section of this course as offered in a recent semester, and are provided for the purpose of information only. The exact learning goals for each course section in a specific semester will be stated on the syllabus distributed at the start of the semester, and may differ in wording and emphasis from those shown here. |
| Prerequisites: |
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