Course Description
Provides instruction and insights into data, theory, and application of machine learning algorithms and skills to apply these algorithms to real world datasets and applications in Engineering. The course also provides hands-on experience through project work.
Additional Requirements for Graduate Students:
Graduate students will also learn topics pertaining to survey of
research approaches in machine learning and deep learning and
targeted research on predictive applications in the industrial
space. These will be evaluated via an additional research
project.
Athena Title
Engineering Informatics
Equivalent Courses
Not open to students with credit in INFO 4150E or INFO 6150E
Prerequisite
Permission of department
Semester Course Offered
Offered every year.
Grading System
A - F (Traditional)
Course Objectives
1. Data preprocessing, data wrangling management, and data representation. 2. Design ML-based predictive models and decision support solutions. 3. Implement solutions for real-world Engineering datasets on a project work basis. 4. Visualize data and insights from analytics and develop a storyline to present results. 5. Prepare and present results based on specific queries and rubric. 6. Creating scalable solutions and parallel computing techniques.
Topical Outline
1. Data Management 2. Structured and Unstructured Data, Meta-Data 3. Data Visualization and Visual Inferencing 4. Supervised, Unsupervised, Reinforcement, and Deep Learning 5. Programming Tools and Libraries for Accomplishing Tasks 6. Regression, Classification, and Clustering 7. Predictive Analytics, Decision Support Systems, and Recommender Systems (Graduate only) 8. Survey of Research Approaches in Machine Learning and Deep Learning 9. Targeted Research on Predictive Applications in the Industrial Space.