CS 6310 - Foundations of Machine Learning

Institution:

Utah Tech University

Subject:

Computer Science

Description:

This course discusses best practices in end-to-end machine learning, including data processing, model selection, validation, and production. This course also discusses various machine learning models including linear regression, ensemble methods, and neural networks. **COURSE LEARNING OUTCOMES (CLOs)** At the successful conclusion of this course students will: 1. Apply the core mathematics concepts required for machine learning, including multidimensional calculus, linear algebra, and probability, to analyze a machine learning algorithm. 2. Formulate a data driven question and implement an end-to-end machine learning pipeline to address that question. 3. Apply supervised learning models, including linear and logistic regression, decision trees, and support vector machines, to address regression and classification problems. 4. Utilize ensemble methods to improve model quality in machine learning pipelines. 5. Construct and utilize neural network models to address regression and classification problems, involving architecture selection, model optimization, visualization, and evaluation. Prerequisites: Acceptance to the Graduate Certificate in Applied Artificial Intelligence and Machine Learning. FA

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.utahtech.edu/

Phone Number:

(435) 652-7500

Regional Accreditation:

Northwest Commission on Colleges and Universities

Calendar System:

Semester

General Education

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