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Machine Learning Certificate

Online Graduate Certificate in Applied Machine Learning

MSOE’s Graduate Certificate in Applied Machine Learning is an accelerated program that provides a firm foundation in data science, machine learning, deep learning and AI with an emphasis on applications. Born out of employer demand, the program blends practical skills with theoretical knowledge to prepare graduates as effective practitioners today and for continued learning tomorrow. Students can expect to lead technical machine learning projects and spearhead adoption across their organizations. The program rapidly develops practical skills in as little as nine months by building on students’ existing technical backgrounds.

The certificate program consists of two application-oriented courses that fuse concepts from statistics and computer science to design algorithms and software that process data, make predictions and aid decision making. Coverage of machine learning concepts is supported by practical, hands-on projects guided by experienced faculty. When you complete this program, you will be able to synthesize solutions in the artificial intelligence and machine learning domains and connect the knowledge you have gained throughout the program directly to your professional work.

Online Applied Machine Learning Graduate Certificate Details
  • Offered 100% online
  • Synchronous evening classes
  • Complete in 9 months (two semesters) at a pace of one course per semester
  • Build on prior knowledge to develop deep expertise at an accelerated pace
  • Combine new knowledge in data science, machine learning and AI with your domain expertise
  • Learn directly from student-oriented faculty who are experts in machine learning
  • Apply credits earned toward the online Master of Science in Machine Learning program
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MSOE and You: Better Together

Earn your master’s or certificate in machine learning online with MSOE. Complete the form to get a program details sheet for the program of your choosing—Master of Science in Machine Learning or Graduate Certificate in Applied Machine Learning—delivered to your inbox.

Build on Your Experience With a Machine Learning Graduate Certificate

This program is for experienced professionals and recent graduates interested in developing new skills and elevating their careers. Does this sound like you?

If you completed a bachelor’s degree in a technical field such as computer science or electrical engineering, have completed at least a one year of college-level programming course work, or have professional software development experience, this program may be a good fit for you. Learn more about the prerequisites for this program by visiting our admissions page.

“My background is in circuits and controls systems, and I have a fair amount of programming experience. Throughout the courses, it was easy to apply my current technical knowledge while learning a whole new set of skills.”
Max M., Graduate Certificate in Applied Machine Learning ’22

Graduate Certificate Learning Outcomes

The Applied Machine Learning Graduate Certificate program prepares you to:

  • Collect and prepare data for analysis
  • Create visualizations and perform statistical analyses to support data-driven decision making
  • Communicate results and actionable insights to non-technical audiences
  • Analyze technical problems to identify appropriate modeling techniques and develop solutions that meet requirements from resource-constrained embedded systems up to large-scale web services with millions of users
  • Develop and evaluate new predictive models using cutting-edge approaches such as deep learning
  • Learn problem-solving strategies and techniques for improving model prediction performance
  • Learn how to apply machine learning to diverse data types such as spreadsheets, images, text, sensor readings and more

Grow Your Knowledge. Strengthen Your Career.

Connect theory to application with MSOE’s Graduate Certificate in Applied Machine Learning. You will take one course per semester, giving you the time and focus to master the concepts being taught by our expert faculty members. Through hands-on learning and in-depth projects, you will complete the program with high-value skills that employers need to drive business forward.

Explore the curriculum

Man in glasses looking at multiple desktop screens of code

Pathway to a Master’s in Machine Learning

The online certificate program is an excellent pathway to a bright future working with machine learning and AI technologies, but it is also a stepping stone for the Master of Science in Machine Learning. The certificate to master’s pathway allows you to pursue your education step-by-step with breaks in between. Use what you learned from the certificate today, and return tomorrow to continue expanding your skill set.

Explore the master's program

RJ Nowling
Meet the Graduate Certificate in Applied Machine Learning Program Coordinator
RJ Nowling, Ph.D.
Office: DH-421
Phone: (414) 277-7318
Email: nowling@msoe.edu

Dr. RJ Nowling is an assistant professor of computer science at MSOE. In collaboration with students and external research groups, RJ applies machine learning and data science to genomic data with the goal of extracting interpretable knowledge.

Prior to joining MSOE, RJ worked on applications of machine learning in digital ad targeting and incorporation of machine learning into web services at companies like Red Hat and AdRoll. He earned a Ph.D. in Computer Science and Engineering from the University of Notre Dame and a B.S. in Computer Science and Mathematics from Eckerd College. RJ teaches courses in data science, machine learning and algorithms.
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Admissions

Learn more about the admissions requirements and process to expand your current skill set with machine learning expertise.
View admissions requirements
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Tuition and Financial Aid

Review the competitive tuition costs and your financial aid opportunities for the machine learning programs.
View tuition and financial aid information
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M.S. in Machine Learning

Review the specifics of MSOE’s Master’s in Machine Learning to see if it’s the right fit for you.
Explore the master’s program

Machine Learning Graduate Certificate FAQs

What is a graduate certificate in AI and Machine Learning?
The graduate certificate in AI and machine learning, officially titled “Graduate Certificate in Applied Machine Learning” at MSOE, is a two-semester, two-course program designed to give you applicable skills in AI and machine learning domains. This program provides a firm foundation in data science, machine learning, deep learning and AI with an emphasis on applications.

What will I learn in MSOE’s Applied Machine Learning Graduate Certificate Program?
Through two courses—AI Tools and Paradigms and Applied Machine Learning—you will learn to design algorithms and software that processes data, makes predictions and helps make decisions. You will learn to synthesize solutions in the artificial intelligence and machine learning domains. The skills you gain through the curriculum are further developed by projects that you can relate to your current work.

What are the prerequisites for the Machine Learning Graduate Certificate?
In order to be eligible for the Graduate Certificate in Applied Machine Learning, you must have:
  • A technical bachelor's degree
  • Programming experience with an object-oriented language—one year of college course work or equivalent minimum
  • At least one year of differential and integral calculus is required. Multivariable calculus or linear algebra is preferred

What skills will I gain from the Machine Learning Graduate Certificate?
Upon completion of this program, you will be able to:
  • Create visualizations and perform statistical analyses that effectively support data-driven decision making
  • Communicate with non-technical audiences, sharing results and actionable insights
  • Identify appropriate modeling techniques and develop solutions that meet requirements from resource-constrained embedded systems up to large-scale web services with millions of users
  • Develop and evaluate new predictive models using deep learning and other cutting-edge approaches
  • Improve model prediction performance with problem-solving strategies and techniques
  • Apply machine learning to diverse data types

Machine learning is taking off globally. Join in and grow your career.

This is an exciting time to build a career working with machine learning and AI technologies. Get the skills you need to lead in this rapidly growing area of technology with MSOE.
Apply Now

Admissions Dates and Deadlines

Jan
17
Application Deadline
January 17
Spring 2025
Jan
21
Start Date
January 21
Spring 2025

Milwaukee School of Engineering has engaged Everspring, a leading provider of education and technology services, to support select aspects of program delivery.

Online Applied Machine Learning Graduate Certificate Curriculum

The courses for the Graduate Certificate in Applied Machine Learning fuse concepts from statistics and computer science to teach you how to design algorithms and software that process data, make predictions, and help make decisions. After completing the two courses that make up the machine learning curriculum, you will be able to synthesize solutions in the artificial intelligence and machine learning domains, connecting the knowledge you have gained in the program to your professional work.

CSC 5610—AI Tools and Paradigms (4 credits)

Course Description

This course introduces topics, tools, languages, and methods used in modern AI practice. Subjects of study include a survey of tools used to perform analysis, data preparation, and visualization along with the algorithms, concepts, and theory underpinning AI. Exercises and assignments will expose students to Jupyter Notebooks and libraries in the Python ecosystem for data science, machine learning, and AI.

Course Learning Outcomes

Upon successful completion of this course, you will be able to:

  • Use a Jupyter notebook environment to write, structure, run, document (with Markdown), and debug Python programs
  • Use Series and Data Frames to load, manipulate, and analyze tabular data sets
  • Leverage appropriate libraries to solve non-trivial data cleaning, preparation, and transformation challenges
  • Employ a plotting library to create simple plots such as histograms, bar or column charts, line plots, and scatter plots including legends, appropriate axis ranges and labels, appropriate font sizes and resolutions
  • Use exploratory data analysis (e.g., summary statistics and visualizations) to characterize data sets and generate hypotheses about the relationships of variables
  • Use a library to train machine learning models and apply models to generate predictions for data
  • Apply and interpret appropriate metrics for evaluating predictions from machine learning models
  • Describe the components of the intelligent agent framework
  • Apply agent and state-based frameworks to solve various problems, such as game playing
  • Provide examples of search problems and techniques for solving them
  • Describe algorithms such as value iteration and q-learning for identifying optimal policies
  • Describe the role of heuristics and describe the trade-offs among completeness, optimality, time complexity, and space complexity
  • Communicate computational results and interpretations in language appropriate for a technical audience
  • Identify ethical implications of potential biases in data sets, algorithms, and applications of AI

Prerequisites by Topic

  • Programming experience

Coordinator: Dr. RJ Nowling

CSC 6621—Applied Machine Learning (4 credits)

Course Description

This course introduces concepts of machine learning that explore the study and construction of algorithms that can learn from and make predictions on data.

Topics include conceptual aspects of machine learning, feature engineering, an introduction to deep learning, and applications of machine learning and deep learning. Students will reinforce their learning of machine learning algorithms with hands-on laboratory exercises for development of representative applications.

Course Learning Outcomes

Upon successful completion of this course, you will be able to:

  • Understand the fundamental concepts and application of supervised, unsupervised, semi-supervised, and reinforcement learning
  • Analyze and describe machine learning algorithms including (but not limited to) logistic regression, SVMs, and decision trees
  • Explain the geometric interpretations of linear and non-linear decision boundaries and their relationship to error metrics (e.g., accuracy)
  • Understand the concepts of learning theory, (e.g., what is learnable, bias, variance, overfitting, curse of dimensionality, splitting data for evaluation of model predictions)
  • Estimate and plot decision boundaries described by trained models
  • Interpret coordinate systems and distinguish between feature and parameter spaces
  • Derive, visualize, apply, and interpret metrics and loss functions to assess the quality of model predictions and fit to data
  • Choose and justify appropriate experimental designs (e.g., test-train split, cross-fold validation) for evaluating machine learning models
  • Encode non-numerical variables in tabular data as numerical features
  • Describe implications of feature representations with respect to linear separability
  • Apply approaches for engineering and evaluating new features from existing
  • Describe the fundamentals of deep learning and what is required to be able to use it
  • Compare various modern deep neural network architectures (e.g., convolutional neural networks (CNNs), encoder-decoders, generative adversarial networks (GANs), or transformers)
  • Describe industry examples where deep learning can be used to solve real-world problems

Prerequisites by Topic

  • Software development
  • Python
  • Jupyter Notebooks

Coordinator: Dr. RJ Nowling