
Online Machine Learning Master's Program
Our Machine Learning Master's program is designed to equip students with the knowledge and skills to pioneer the next technological revolution. As machine learning and related disciplines continue to advance, their impact will soon extend to every facet of technology.
Machine learning is a rapidly expanding field with a vast range of applications across various domains, including intelligent systems, computer vision, speech recognition, natural language processing, robotics, finance, information retrieval, bioinformatics, healthcare, and weather prediction.
Our exceptional Machine Learning Master's program offers a comprehensive curriculum that not only establishes a solid foundation in theoretical concepts but also ensures practical proficiency. You will gain an in-depth understanding of deep learning theory and become well-versed in the most important paradigms. This knowledge will empower you to apply existing methods or develop new approaches for real-world applications. Whether you aspire to pursue a career in industry, academia, or research, our program prepares you to excel in your chosen path.
Below is a suggested term-by-term sequence of courses:
Term 1
Term 2
Additional Core Courses
Choose at least 2 from this list to fulfill the core course requirements:
*Elective Concentration Courses
In addition to the above four required courses, students can choose from three additional elective courses. The following can be found in Academic Catalog and offer an online section:
*PROGRAM’S ELECTIVE COURSES (2) | BIA 654 Experimental Design II (3) BIA 660 Web Mining (3) BIA 662 Cognitive Computing (3) BIA 678 Big Data Technologies (3) CPE 608 Applied Modeling & Optimization (3) CPE 695 Applied Machine Learning (3) FE 541 Applied Statistics with Applications in Finance (3) MA 541 Statistical Methods (3) MA 630 Advanced Optimization Methods (3) MA 641 Time Series Analysis I (3) |
*GENERAL ELECTIVE COURSES (3) | The remaining three courses can be any general elective approved by the student's advisor or academic department. |