Education

Fall semesters: second-year graduate-level course in machine learning.

Spring 2023: second-year graduate course with industry proposed team projects focused on Computing and Data Science for Soft Materials Innovation (co-teaching with lead instructor Prof. Arthi Jayaraman)

Spring 2023: Geospatial Data Science a 300-level undergraduate course in geospatial data science (co-teaching with Prof. Jing Gao and Prof. Gregory Dobler

Spring 2022–: NRT-MIDAS NSF funded research traineeship, serving as Core Faculty and secondary advisor for trainees

Spring 2020, 2021, 2022, 2024, 2025: 300-level undergraduate course in signal processing and linear system analysis

Summer 2021: Independent study on adaptive filters

Spring 2021: Graduate Data Science Community Hour during Spring 2020

Graduate Course

Large-Scale Machine Learning ELEG/FSAN/CISC 817

Advanced graduate technical elective taken by students from multiple programs that improves student understanding in the statistical and computational scaling issues from increases in the dimensionality, amount, and complexity of data.

Developed over summer 2019, offered in the fall semester 2019–2025.

Course Description: Introduction to the analysis and processing of massive high-dimensional data. Massive data sets generally involve growth not only in the number of individuals represented but also in the number of descriptive parameters of the individuals, leading to exponential growth in the number of hypothesis considered. Approaches to address these problems exploit concepts from statistics and machine learning.

The course serves as a capstone course in machine learning for graduate students, with the goal of readying students for publishing their own machine learning and data science research. In terms of content, the course covers computational and statistical scaling of machine learning from both theoretical and practical perspectives as described in the syllabus. The learning assessments provide exercise in key aspects of machine learning research: problem formulation, critical reading of literature, algorithm implementation, abstract writing, experimental design, and peer review.

Summative assessment is based on two mid-term exams and final project.

Anonymous feedback from 2024 course evaluations:

• “The professor was always welcoming for a discussion and doubts both in class and outside of the class, also the professor’s passion and enthusiasm for the subject was infectious as it got us amped for the course too”
• “This machine learning course was invaluable in helping me review and deepen my understanding of mathematically formulating machine learning tasks, particularly in the context of large-scale data for my knowledge of appropriate algorithms, regularization techniques, and key considerations for managing high-dimensional spaces methodically. One aspect I found particularly interesting was the reading assignments, which were then discussed in class. The assigned papers were highly relevant and made it easier for me to follow the course material.”
• “The course was comprehensive, and provided a good survey of ML practices and methods while also still going reasonably deep into each topic.”
• “I really appreciate the enthusiasm, approachability, and overall flexibility of the course. It’s also really easy to see that Prof. Brockmeier is passionate about the subject and has thought about a good “roadmap” to teach it, which is much appreciated. It feels coherent in theme.”

Anonymous feedback from 2023 course evaluations: - “I have rarely encountered instructors with excellent knowledge of the subject matter and excellent communication/ teaching skills. Dr. B is one of those.”

• “Professor Brockmeier has deep knowledge about the topics he is teaching and shares this knowledge very well. The course is very well structured and the method of teaching, reading assignments, coding assignments, and self-picked projects is engaging as well as challenging. He is very open while asking questions. Not only does this attitude set him apart from other teachers, but also the way he understands where the student struggles and the way he gives constructive feedback and ideas to solve those struggles.”
• “His teaching approach is distinctive. Unlike typical courses where materials of comparable quality are readily available on platforms like Google, Coursera, or MIT online courses, this course stands out. The initial segments delve into fundamental materials and papers within the realm of large-scale machine learning. The latter part transitions seamlessly into the explanation of cutting-edge materials in the field. The alignment of homework assignments with the course content is meticulous, featuring well-defined timeframes and providing constructive feedback.”
• “When describing his teaching approach, I would say, he is genuinely invested in fostering a learning environment. His commitment to ensuring comprehension before the semester concludes is evident. Despite the prerequisite background needed for this course, he takes the time to revisit crucial concepts. Demonstrating kindness, patience, and expertise, he actively keeps us abreast of the latest developments in machine learning. His efforts to create a comfortable class atmosphere effectively minimize the typical stress associated with a graduate course, thereby maximizing the learning experience. Having taken classes with various professors, I confidently rank him among the top three.”

Anonymous feedback from 2022 course evaluations:

• “This is one of the most informative classes I have taken so far. Dr. Brockmeier has a very deep understanding of the subject matter. He teaches each topic in detail and provides a lot of additional materials that the students can explore to further their understanding.”
• “This was the best course I took in the MSDS program for all around data science knowledge. This should be a MSDS capstone/required course. I would encourage more MSDS students to take this course. Even if students do not have a lot of experience with AI/ML, Dr. Brockmeier’s instruction and construct of the course will guarantee students will leave with significantly more data science knowledge. The professor’s knowledge and passion for the subject was terrific. The professor’s ability to dive deep into subjects with no notes and no hesitation during lecture was really impressive to watch.”
• “Professor Brockmeier is very helpful with anything. His comments on various assignments were very helpful in giving a different perspective. The course is also very well structured and covers very important topics of Machine Learning.”
• “Well-prepared lectures; I really like the reading assignments. Read high-quality papers and summarize my understanding. In this way, I learn a lot.”
• “The knowledge framwork for various machine learning models is holistic and not simple a copy and paste from other ML courses. Relying on both intuitive cognition and mathematical tools to describe a model and the often inscrutable optimization process. Forcefully pushes me through a process to accomplish a paper (or some sort) by myself, although I’ve done it before, I’m glad to go over it again in a limited time.”

Anonymous feedback from 2021 course evaluations:

• “Prof Brockmeier has done a great job of curating a higher-level data science course for grad students that helped inform me about the origins of the fundamentals of machine learning, the relevant aspects of probability and mathematics that relate to machine learning, and the state of the art methods that are popular right now. The class felt linear and started at a basic level where the instructor introduced fundamental concepts and basic mathematics, and slowly introduced papers that were important in the field. The weekly assigned readings were generally helpful and the homeworks were selected well and reflected material presented in the course.”
• “I very much enjoyed this class and learned a lot. I would describe the class as an introduction to reading ML research papers, which is an extremely useful skill. Professor Brockmeier selected some of the key research papers in statistical learning and would break down the background material and key concepts in class. For homework, we read these papers, much better equipped to understand the technical details. By the end of the semester, we were able to understand some of the key concepts in statistical learning and possess the skills to continue learning and researching for our course projects. This course also helped me in my research, as my advisor is not focused on ML. By being able to understand ML papers and what to look for when reviewing has opened doors for me, as I now feel like I would be able to publish in ML journals. Overall, it was a great course and I highly recommend it to any graduate student whose research touches upon ML”
• “I really enjoyed the class, […] our interaction with the instructor was having a major role in the educational process. The in-class atmosphere and flexibility (time and topics) are top-notch, everything is done to make the course fit as many ML-related majors as possible.”
• “I thought the course was very interesting, and definitely expanded my understanding of machine learning”
• “Clearly a subject matter expert. Communicated material well. I always felt like I could ask questions in class, office hours, or email. Professor showed that he was focused on actual learning beyond what is often demonstrated by other faculty.”
• “He included students in the class and was mindful of both those present in person and on Zoom. He made the class interactive and made sure everybody had a chance to speak their thoughts, questions and opinions about different models. He also fostered a good peer review process which allowed students to review our peer’s projects and provide constructive feedback. He laid out during class what constructive feedback looks like for this process”

Anonymous feedback from 2020 course evaluations:

• “The topics covered in the course are interesting and relevant. The instructor exhibits a deep understanding of the subject and has the capacity to motivate interest in the students.”
• “The instructor did a great job structuring the course and selecting the key concepts and methods among the vast amount of topics in large-scale machine learning. He is very knowledgeable and demonstrates an honest interest in our learning experience. The evaluation tools used (the midterm, the pop quizzes, the homeworks, and the final project) are well designed to accomplish our learning goals. I particularly liked that he made emphasis throughout the course in that the evaluation tools were more intended as learning tools, and offer us options to ease the natural pressure that the grades associated with those tools usually have.”
• “I like the discussions and that despite being online, the professor was capable of keeping me engaged even when the topics were not particularly easy to explain. I appreciate the use of a whiteboard instead of the slides, it gives flexibility to the class and it helps me to understand.”
• “He always wants us understand the topics. He supports lecture[…] interactions. He asks during lectures to make sure we are on the right track. He tried to link topics that are correlated and related to each other. He links the topics we are studying to a real world examples.”
• “I can see Prof. Brockmeier really wants students to learn, no matter whether a student is already a machine learning research[er] in a specific area or is still exploring the subject.”
• “Dr. Brockmeier is a great professor. He was well prepared for the classes and was capable of address almost every question that pops up during the course and always made a good connection between theory and Matlab examples.”
• “There will be timely comments and replies to every student’s questions and papers”
• “He was open to listening to the questions of all the students that wanted to ask and address those questions in a respectful way.”
• “He was always polite and gave everyone the opportunity to express their opinions and ideas regarding the topics that were explained.”
• “I rank him in the top 1 percentile for research ability, approachability and overall being a good person. I would take his class again and recommend it people.”

Anonymous feedback from 2019 course evaluations:

• “The course really dives deep into the mathematics and theory of the machine learning techniques. The organization of the course was nice.”
• “Course material was well ordered, well motivated, and useful.”
• “The breadth of topics studied in this class provided me with a better understanding of machine learning in general.”
• “Professor Brockmeier […] was able to connect abstract concepts from the class to multiple applications to motivate understanding”
• “This was not only the best course I took as a graduate student, but moreover, the most useful in the pursuit of becoming a machine learning expert.”
• “He was enthusiastic about delivering the contents of the course.”

Undergraduate Courses

Signals and Systems ELEG 305

This course examines continuous and discrete-time signals and systems at the introductory level.

Course description: Introduction to signals and systems, with an emphasis on time and frequency characterization of linear, time-invariant systems. Covers discrete and continuous time systems, sampling, and Fourier, Laplace, and Z transforms.

Learning Outcomes

Students will be able to apply detailed analytical techniques, in both time and frequency, needed to characterize continuous and discrete-time signals and systems.

This course addresses the following ABET student outcome:

1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering,
   science, and mathematics. Specifically,

• 1.5 Apply knowledge of science and math towards problems in signal processing and communications

By the end of this course students will be able to

• Analyze, relate, and visualize signals—Recognize properties of continuous and discrete-time signals and convert between their mathematical, graphical, and numerical representations in time, frequency, and Laplace/ Z domains.
• Characterize and represent systems—Analyze systems (and their effect on idealized inputs) whether they are expressed analytically through formula, code, or depicted as block diagrams, and convert between equivalent representations.
• Compute system responses—Describe the effect of various transformations in different domains (time, frequency, etc.) on signals, especially computing the output of linear time-invariant systems.
• Analyze filters—Calculate the magnitude and phase response and analyze the properties of basic filters in continuous and discrete-time using appropriate transforms.
• Determine sampling and analog-to-digital conversion parameters—Choose an appropriate sampling rate and filtering in order to uniquely preserve the information of bandlimited signals and describe the consequences of undersampling (aliasing).
• Perform numerical programming for signal processing—Use modern programming languages to describe signals and systems for computer-based analysis, processing, and visualization.

Core requirement in the following programs:

• Electrical Engineering
• Computer Engineering
• Cybersecurity Engineering
• GIScience and Environmental Data Analytics

Prerequisite for subsequent courses:

• ELEG 306 - Digital Signal Processing
• ELEG 403 - Communication Systems Engineering
• ELEG 405 - Engineering Machine Learning Systems
• ELEG 404 - Digital Imaging and Deep Learning
• ELEG 418 - Digital Control Systems
• ELEG 492 - Radar Systems and Technology
• BMEG 441 - Biomechatronics†
• BMEG 330 - Biomedical Instrumentation†
  † As an alternative for BMEG 230

Anonymous feedback from 2025 course evaluations:

• “One of my best professors”
• “Professor Brockmeier is extremely knowledgeable on the subject and it shows. Hes also super nice and helpful.”
• “Professor Brockmeier is a wonderful professor that made this signals class greatly interesting to me. He was great at not only teaching the topic but also communicating real world examples and uses of signal processing, which helped really understand why we are being taught signals and systems”
• “He is very knowledgable on the subject, it is just a very hard topic to generate interest in as it is honestly very dull and difficult course work. However, I do feel like the Professor does try his best genuinly so I do appreciate that.”
• “Professor Brockmeier is definitely the most personable of any of my professors, and I appreciate the way in which he presents the material. He runs through many examples to illustrate his points. I think he’d benefit from a different room with a microphone that works more consistently.
• “The instructor answer every questin and tried his best at explainig the topics when students where confuse, also he worked with practice exams in class to prepare us for midterms which was very helpful too”
• “Dr. Brockmeier is a very smart man, he teaches a challenging course but provides his students with a lot of valuable resources that make the class as enjoyable as it can be.”
• “The professor was great he was very knowledgeable and very friendly his attitude towards the class made me want to do better and prove myself, his censored quizzes and exams are genuinely one of the best things about the class as it takes the guesswork out of knowing what he expects from us, Absolutely fantastic professor”

Unsolicited Feedback from 2025 section:

• “I appreciate all you have done for me this semester with bolstering my concepts of electrical engineering and signals and systems circuits. I knew this class was going to be challenging and I felt right at home having you teach his class. Your teaching was very easy to follow and encouraged me to pursue electrical engineering in the future. I felt like I was learning a lot with the examples that you set in your lecture notes this semester.”

Unsolicited Feedback from 2020 section:

• “I want to applaud your tenacity and optimism throughout the term. Despite this class being the most challenging course I’ve ever taken, I have enjoyed being your student.

Honors Discussion Section: Students registered in Honors for this class will be involved in an additional discussion section, which introduces content beyond the general class: specifically, non-linear, adaptive, and random systems. They will also be required to complete a semester long project that reflects an application of such systems.

Anonymous feedback from 2025 Honor’s course evaluations:

• “He promotes good discussions on real-world applications of the course material, facilitating an easier research process.”
• “He set up an honors discussion section in which we picked project ideas related to but slightly beyond the course material. This was an interesting experience and helped show the real world application of what we are learning in class.”
• “He offered Honors sessions in his office to go more in-depth with concepts adjacent to the syllabus material, and we had to make our own relevant presentation and record it.”

Anonymous feedback from 2021 Honor’s course evaluations:

• “The weekly discussions and individual research project helped simulate interest in various topics to do with signals and systems. Was one of the many reasons why I’ll be switching majors to ELEG.”
• “The Honors discussion was my favorite class in the entire semester because it felt so personable and I was able to focus on my own paths of research.”
• “I really enjoyed the honors discussion section for this class. I found it to be a nice place to talk with the professor in a more personal setting and learn about some interesting concepts that weren’t covered in the main section of the class.”

Geospatial Data Science ELEG/CISC/GEOG/PHYS/SPPA 367

An undergraduate technical elective (experimental) offered online to UD and Lincoln University students as part of an NSF Data Science Corps grant 2123264

I provided one-third of the content covering geospatial data science programming and machine learning and coordinated the overall design and student assessment (educational material—slides and Juypter notebooks—hosted on GitHub) This was an introduction to programming, machine learning, or data science for many students, and the students appreciated the learning environment. Each lecture session was paired with a computer-programming based lab. This required them to review some material themselves, but ask questions if needed.

Anonymous feedback from course evaluations:

• “Amazing!”
• “Was a very good instructor”
• “Good professor overall”
• “Having engaging lectures…the use of participation points/check your understanding sections were good mental checkpoints, especially with the wealth/shear amount of information that was being presented during his section. Having demos to each of the labs was also a big help with the complexity of the material. I could see, for the most part, how techniques talked about in lecture were directly applied to the labs. The “checking understanding” bit also extends to the Mock Exam, which served as a good starting point to studying the Midterm.”
• “He was great at helping you with the labs as I found his the most difficult of the three professors.”
• “The second professor [Brockmeier] used a lot more participation which make it easier for me to understand what I needed to know and gave me more of an understanding of what we were doing.”

Independent Study ELEG 466

“Adaptive Filtering as an Introduction to Machine Learning”

Offered: Summer 2021

Course description: Introduction to adaptive filter theory and application. Students will create a technical report that details the theory, implementation, and computational experiments with the application of adaptive filtering. Topics to be covered include review of delay lines, z-transform, multivariate signal processing, Gaussian white noise, correlation matrices, whitening transforms, least squares, convex optimization, Wiener filter, stochastic gradient descent, least mean squares (LMS), NLMS, common spatial patterns, and various types of recurrent memory. A comprehensive technical report with equations and figures will be typeset in Latex. The report will detail the problem formulations, equations, and vector graphics of block diagrams, plots of results. Software implementations will be made in MATLAB/Octave, Julia, python/Numpy, or python/Pytorch. Synthetic experiments will be conducted for noise cancellation, channel estimation, and machine learning.

Learning outcomes: While students typically learn theory and fundamentals of signal processing and filtering in ELEG305 “Signals and Systems” and probability theory and statistics related to signals in ELEG310, due to time constraints and curriculum design, students rarely have the opportunity to learn and apply adaptive filter theory that combines filtering, linear algebra, optimization, and statistics together. This combination is the foundation of machine learning theory and algorithms. This independent study will attempt to fill the gap in the current curriculum areas in theory and practice. A combination of synthetic data and real-world projects will be explored by students to master these concepts. Students will practice their understanding by describing the formulations and results (equation and paper typesetting, block diagrams, data visualization and presentation) in their own words as well as implementing and testing software for adaptive filtering and machine learning. In summary the students will learn the foundations and techniques of machine learning: linear and non-linear filtering, supervised learning, least squares, optimization, stochastic gradient descent, batch normalization, and experimental design (training, validation, and testing).

Experience: The course will solidify knowledge learned in other courses and provide the student with a diverse and broad understanding of adaptive filtering, and an introduction to machine learning. The course project will require tools learned in other academic classes (signal and systems, linear algebra, calculus, probability, and statistics) and will help the student in future classes (communication systems, machine learning, control theory) as well as research.

Expectations: Students will be required to complete a technical report which will be a culmination of most of the topics covered in class. Weekly revised versions of the technical report must be submitted to document learning progress until the end of the course. Each of these weekly submissions, will require an executive summary that overviews the specific learning activities, progress in understanding and experiments, obstacles, and interesting insights or useful skills that have been learned. Students can work in groups and collaborate in a civil and equal manner.