Artificial Intelligence Certificate Program

Recent advances in and integrations of machine learning and artificial intelligence are significantly impacting the ways that data scientists build and program computer software, statistical models, and neural networks. Each of these areas is comprised of different analytical approaches; artificial intelligence utilizes rules-based, logic, and knowledge-based systems, while machine learning relies upon data-adaptive methods such as naïve Bayes models, nearest neighbor models, classification and regression trees, random forests, support vector machines, and neural networks. Data-adaptive models are the next step in the progression of modeling and analytics, from traditional statistics to data mining to machine learning, where computer software learns from data and makes traditional models more flexible. Many of today’s prominent applications of artificial intelligence – including computer vision, natural language processing, and robotics – rely on deep learning, the area where machine learning and artificial intelligence overlap.

Please note that courses completed in the certificate program cannot be transferred to the corresponding graduate degree.

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About the Artificial Intelligence program

Artificial Intelligence Course Schedule

The Artificial Intelligence Course Schedule page provides you with detailed information on the program's offerings.

Artificial Intelligence Learning Outcomes

The Artificial Intelligence Certificate of Advanced Graduate Study has been designed to ensure that students who successfully complete the certificate can:

  • Utilize methods of machine learning and deep learning to build and run analytical models and neural networks;
  • Apply natural language processing techniques to speech recognition, semantic processing, text classification, and sentiment analysis;
  • Process image data for visual exploration, image classification, remote sensing, and facial recognition;
  • Utilize automated systems for robotics applications with special emphasis on reinforcement learning and the training of robots to mimic human behavior.

Artificial Intelligence Curriculum

Students are required to complete the following four courses to earn the certificate:

  • MSDS 453 Natural Language Processing
  • MSDS 458 Artificial Intelligence and Deep Learning
  • Two of the three courses below:
    • MSDS 459 Knowledge Engineering
    • MSDS 462 Computer Vision
    • MSDS 464 Software Robotics

Review curriculum details while you consider applying to this program. Current students should refer to the curriculum requirements in place at time of entry into the program.

Artificial Intelligence Faculty

You can find a full listing of our instructors in this certificate program on the Artificial Intelligence Faculty page.

Admission for the Artificial Intelligence program

Applicants to this certificate program must hold a graduate degree from an accredited U.S. college, university or its foreign equivalent. A competitive graduate record that indicates strong academic ability is required. Work, internship, or research experience is highly desirable, but not a requirement. Work, internship, or research experience is highly desirable, but not a requirement. A list of admission requirements can be found on our Artificial Intelligence Admission page.

Artificial Intelligence Tuition

Tuition costs can vary for each of our programs. For the most up-to-date information on financial obligations, please visit our Artificial Intelligence Tuition page.

Artificial Intelligence Registration Information

The Artificial Intelligence Registration Information page outlines important dates and deadlines as well as the process for adding and dropping courses.

Additional Information

Prior to applying to this program, students must have completed MSDS 420 and 422 or possess equivalent knowledge and skills. Please see below for more information:

MSDS 420 Database Systems
This course introduces data management and data preparation with a focus on applications in large-scale analytics projects utilizing relational, document, graph, and graph-relational databases. Students learn about the relational model, the normalization process, and structured query language. They learn about data cleaning and integration, and database programming for extract, transform, and load operations. Students work with unstructured data, indexing and scoring documents for effective and relevant responses to user queries. They learn about graph data models and query processing. Students write programs for data preparation and extraction using various data sources and file formats. Recommended prior programming experience or 430 Python for Data Science. Prerequisites: None.

MSDS 422 Practical Machine Learning
The course introduces machine learning with business applications. It provides a survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification. Prerequisites: MSDS 400-DL Math for Modelers and MSDS 401-DL Applied Statistics with R.

Find out more about Northwestern's Artificial Intelligence program

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