About the Academy of Machine Learning
Machine learning (ML) is an emerging field that has profoundly impacted our society. The ubiquitous SIRI, Amazon Echo and Google use ML for voice recognition, while Waymo and other self-driving vehicle systems use ML for object detection and decision making. Other companies like Facebook and Netflix are using ML for targeted advertising. These are just a few examples of the marvels of ML, and it is expected to grow even more as new data from various sources become available.
The ever growing dimensions of modern-day data have created critical challenges in various fields of natural sciences and engineering in recent years. Within the field of engineering, storage and privacy considerations, data mining and signal processing bottlenecks have initiated a paradigm shift towards data-centric solutions, often referred to as “Data Science”. In particular, approaches based on ML have proven successful in numerous applications and play a central role in this new paradigm due to their ability to harness the sheer dimensionality of data.
Machine learning combines probability and statistics, large data analytics, optimization techniques, and computer algorithms. Our ML program is designed to provide a concentration of courses around these topics and incorporate a real-world design experience. The program is open to all University undergraduate students that meet the admissions requirements.
Notation Requirements & Courses
The Academy of Machine Learning will have 12-13 credits of required coursework, of which 6 credits (Machine Learning and Machine Learning Design) must be unique to the ML program. Prior to enrolling in the program, students will need to complete three program admissions requirements.
The program will be open to any student that has a UMD cumulative GPA average of 3.2 after 60 credits of undergraduate education and completion of the following courses with a minimum grade of “B-” or better:
ENEE150 Intermediate Programming Concepts for Engineers (3 credits) or CMSC216 Introduction to Computer Systems (4 credits)
MATH141 Calculus II (4 credits)
ENEE222 Elements of Discrete Signal Analysis (4 credits)
ENEE324 (Engineering Probability, 3 credits) or STAT400 (Applied Probability and Statistics I, 3 credits)
ENEE351 (Algorithms and Data Structures, 4 credits) or CMSC351 (Algorithms, 3 credits) or ENEE469O (Introduction to Optimization, 3 credits)
ENEE439M (Foundations of Machine Learning, 3 credits)
ENEE439D (Machine Learning Design, 3 credits)
In order to complete the Academy of Machine Learning, students must complete all 12-13 required credits with a minimum GPA of a 3.0 in all program courses. Each course must be completed with a minimum grade of a "B". Finally, both the Machine Learning (ENEE439M) and Machine Learning Design (ENEE439D) courses must be unique to the program, meaning that they cannot be double counted for major requirements.
Students interested in applying to the program must have completed at least 60 semester credits, satisfied all course/grade requirements, and have a minimum grade point average of 3.2 at UMD for admissions consideration. The program will review applications holistically and will be looking for applicants with a strong academic performance. The program will accept applications for both fall and spring semesters. Please check back for details about application deadlines.
Applying to the Program
The application for the Academy of Machine Learning will be available online on February 24th, 2020! The deadline to apply is April 17th, 2020.
Registration Information, Forms, Additional Program Information
Students outside of ECE who are interested in pursuing the Academy of Machine Learning can email email@example.com
Program Completion and Transcript Notation
Upon successful completion of the Academy of Machine Learning program, students will receive a notation on their academic transcript. The notation will read: Academy of Machine Learning Distinction – Department of Electrical & Computer Engineering.
Students with questions about the program can e-mail us at firstname.lastname@example.org.
More information coming soon.