Online AI CurriculumOnline AI CurriculumOnline AI Curriculum

The AI certificate curriculum was developed by the world-class faculty at Columbia Engineering — a top-ranked university for artificial intelligence programs¹ as well as online graduate engineering programs.²

The AI executive certificate program will prepare you to amplify your technological expertise and apply it across industries. You will discuss emerging AI research and trends, exchange ideas with cross-industry peers, and learn how to integrate AI into an organizational strategy.

After you finish this online program, you’ll have access to alumni benefits throughout your career,  including a lifelong Columbia affiliation and email address.

This non-credit, non-degree executive certificate program comprises one (1) self-paced bridge course, six (6) core courses with live classes and asynchronous coursework, and an essential immersion experience, held on the Morningside Heights campus of Columbia University in Manhattan.

The program is ideal for working professionals with demanding schedules. Learners study part time, taking one (1) course per term to complete the program in as few as 18 months.

Course titles and content in the online AI executive education program are subject to change.

• Bridge Course: Technical Foundations

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  Course goals: The goal of this bridge course is to ensure that participants meet the prerequisites for other courses in this program. The material in this course serves as a review and refresher of basic concepts in programming, data structures, and math (discrete math, calculus, linear algebra, and probability theory).

  Exams and assignments: Five (5) pretests and five (5) posttests in Python, data structures, calculus, linear algebra, and probability. This course is ungraded; however, we expect learners to complete all posttests. If you score lower than 65% on any posttest, we strongly encourage you to review the segment and retake the assessment.

  Topics covered: programming in Python, data structures, discrete math, calculus, linear algebra, and probability.

• Core Course 1: Intro to AI and Business for AI

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  Course goals: Learn AI fundamentals and key ideas behind the design of intelligent agents for real-world problems, including search, games, machine learning, and constraint satisfaction. Gain exposure to applications of AI and machine learning in business—such as customer service, sales, and marketing—and study how AI is used in other industries like retail, finance, health care, and manufacturing.

  Exams and assignments: Learners will complete eight (8) quizzes (50%) and three (3) assignments (50%).

  Topics covered: history and evolution of AI, search, constraint satisfaction problems, adversarial search, machine learning, logical agents, and AI ethics.

• Core Course 2: Algorithms and Machine Learning

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  Course goals: In the first half of the course, you will learn how to design and analyze algorithms, with the goal of developing an algorithmic mindset: how to think from specification of a problem to designing an efficient algorithm for it. In the second half of the course, you will focus on supervised learning techniques for regression and classification on real-world data sets.

  Exams and assignments: Learners will complete four (4) homework assignments, each worth 25% of the total course grade. The passing grade for this course is 70% or higher.

  Topics covered: performance of algorithms; design techniques for optimization problems; graph algorithms; linear programming and hardness; overview of machine learning; statistical models for prediction; linear models; and inductive biases, optimization, and objectives.

• Core Course 3: Neural Networks and Deep Learning

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  Course goals: Learn about the theoretical and programming aspects of neural networks (ANNs) and deep learning (DL) models. This course explores the invention, history, and development of ANNs and DL models; describes their relationship to machine learning; and identifies the ways they can be used to solve a variety of industry and business problems.

  Exams and assignments: Learners will complete five (5) assignments (50% of course grade); eight (8) quizzes (20% of course grade); and one (1) final exam (30% of course grade).

  Topics covered: neural networks; deep learning; machine learning; deep forward networks; learning and back propagation; convolutional neural networks; optimization, regularization, and practical methodology; object detection; recurrent neural networks; auto-encoders, generative models; and generative adversarial networks.

• Core Course 4: Natural Language Processing and Speech

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  Course goals: This course provides an introduction to the field of natural language processing (NLP). Learners will learn how to create systems that can analyze, understand, and produce language. In addition to machine-learning methods for NLP, this course covers a range of applications (e.g., information extraction, machine translation, text generation, and automatic summarization) and the role of linguistic concepts.

  Exams and assignments: Learners will complete three (3) assignments (75%). Class participation accounts for 25% of the course grade. The passing grade for the course is 65% or higher.

  Topics covered: language modeling; text classification, sentiment, and neural nets; word embeddings and sequence modeling; semantics and machine translation; extractive summarization; abstractive summarization and language generation; information extraction and dialogue; and bias and industrial applications.

• Core Course 5: Computer Vision and Robotics

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  Course goals: Understand the fundamentals and the applications of robotics and computer vision. This course will cover the underlying theory and principles of the sensorimotor loop, and translate those learnings into practice. Learners who successfully complete this course will gain an understanding of how many of the state-of-the-art robots and computer vision systems work.

  Exams and assignments: Learners will complete four (4) homework assignments (50%) and four (4) quizzes (50%). The passing grade for this course is 65% or higher.

  Topics covered: robotics, motion planning, robot simulation, computer vision, object recognition, geometry, video, and robot learning.

• Core Course 6: Data Privacy, Security, and Policy

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  Course goals: Gain an understanding of the security, privacy, and policy aspects surrounding machine learning systems. In addition to the legal requirements for privacy and security in different jurisdictions, this course addresses the security and privacy attacks that can affect machine learning models, and advanced privacy technologies that can be used to increase data protection and personal privacy.

  Exams and assignments: Learners will complete six (6) exercises worth a collective 72% of the course grade. Class participation accounts for 28% of the course grade. The passing grade for the course is 65% or higher.

  Topics covered: law and policy frameworks (e.g., GDPR, HIPAA); security and privacy; advanced privacy technologies; machine learning attacks and defenses; and differential privacy.

A cornerstone of the online AI certificate, this three-day, in-person immersion is an invigorating experience in New York City, a hub of innovation, creativity, and research.

Held on the Morningside Heights campus in Manhattan, the immersion features working sessions, presentations, networking opportunities, and community-building events with peers, instructors, and alumni.

The online AI executive certificate is a non-degree program and does not qualify for student (female or male) visas. Individuals are not permitted to study on visitor or visa waiver (ESTA) status. 

To learn more about visa requirements, email the International Student and Scholar Office, or visit the U.S. Department of State website.

Even after you finish the program, you will have access to exclusive Columbia Engineering resources and benefits, which include: