Machine Learning Programming For Real-World Applications

This course aims to introduce students to practical tools used to solve various types of machine learning problems. This course focuses on both standard machine learning techniques and deep learning methods. The applications being explored are data imputation, natural language processing, object recognition, and trajectory optimization. Students will work on a semester project in which they must apply some of the tools to a problem area of their choosing, with the expectation of a resulting conference paper. These projects will illustrate that students are able to use these methods to effectively solve modern problems. (Taught Spring 2019, Co-Instructor: Dr. David Feil-Seifer, Approx. 21 students).

Upon completion of this course:

• Students will be able to explain the major machine learning methods including KNN, linear regression, SGD, K-means clustering, PCA, MLPs, CNNs, and Q-learning.
• Students will be able to apply machine learning methods to solve real-world problems.
• Students will be able to evaluate the suitability of different machine learning methods for various applications such as data imputation, NLP, object recognition, and trajectory optimization.

Course content developed by Janelle Blankenburg under the advisement of Dr. David Feil-Seifer (Fall 2019).