Online AI CurriculumOnline AI CurriculumOnline AI Curriculum

The AI certificate’s curriculum strives to enable discussion of emerging AI research and trends with world-class Columbia faculty and instructors, facilitate valuable cross-industry collaboration among peers, and help technical leaders integrate AI into their organization’s strategic planning decisions.

This non-credit, non-degree executive education program is composed of 1 self-paced bridge course, 6 core courses with live and asynchronous coursework, and an essential immersion experience at Columbia University’s Morningside campus in Manhattan. You may choose to complete the program full time (2 courses per term) or part time (1 course per term).

A state-of-the-art digital learning platform brings the curriculum to life through one-click access, live classes, offline learning, robust search and collaboration capabilities, and tech support.

6 courses

1 bridge course

1 in-person immersion

9–18 months to complete

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Bridge Course

Technical Foundations

This self-paced asynchronous course is strongly recommended for all learners to help them ensure a solid knowledge base before beginning the online AI program. The syllabus covers statistics, linear algebra, multivariable calculus, probability, data structures, basic programming in Python, and foundational topics in math that are relevant to the content in the core courses.

You should expect to review the mathematical and technical coursework as well as complete the self-assessment. The content from the bridge course will be available for the duration of the program so that you can refresh your understanding of these topics at any time.

By the end of the bridge course, learners will be able to:

  • Write Python scripts and programs that process, clean, manipulate, and visualize data as needed for AI applications.
  • Understand and use functions in Python, including the scope of parameters and variables.
  • Explain and interpret mathematical concepts that are foundational to AI algorithms and approaches.
  • Build programs that use object-oriented programming concepts to inform their design.
  • Use Pandas DataFrames and functions to analyze datasets and aggregate data.
  • Create visualizations using Matplotlib.

Core Courses

The core of the curriculum is designed around knowledge-building through world-class instruction, thought-provoking discussion, and peer collaboration. To help learners reinforce their knowledge, and to measure each participant’s progress, instructors use a variety of assessments, including diagnostic quizzes, as well as programming, coding, and data structures exercises, and final projects.

Intro to AI and Business for AI

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.

By the end of this course, learners will be able to:

  • Communicate efficiently with business stakeholders, IT specialists, and data analysts on how to create an AI solution.
  • Articulate the process of constructing sentiment engine and facial recognition systems.
  • Show a strong awareness of challenges as well as of ethical and policy issues businesses may face while developing AI solutions.
  • Discuss ongoing research into the ethics of AI, including explainable AI (XAI), correcting bias in data, and inclusive AI.
  • Describe strategies for modifying a chatbox to improve customer experience.
  • Understand potential challenges in building an AI system for customer service.
  • Compile and evaluate an AI team.

Algorithms and Machine Learning

Learn fundamental ideas of design and analysis of efficient algorithms, including sorting and searching, graph algorithms, and dynamic programming. After going over general algorithmic approaches, focus on supervised learning techniques for regression and classification on real-world datasets.

By the end of this course, learners will be able to:

  • Recognize problems for which machine learning may be suitable.
  • Analyze algorithms to determine and verify which are more effective than others.
  • Discuss fundamental ideas of design and analysis of efficient algorithms, including sorting and searching, graph algorithms, and dynamic programming.
  • Focus on supervised learning techniques for regression and classification on real-world datasets.

Neural Networks and Deep Learning

Study the nature of deep learning (DL) and neural networks, explore applications for both, and identify ways in which DL and neural networks can be applied to solve industry or business problems.

By the end of this course, learners will be able to:

  • Know the theoretical underpinnings as well as the architecture, performance, datasets, and applications of neural networks and deep learning.
  • Become familiar with TensorFlow deep learning framework and the Google Cloud computational platform, with graphics processing units (GPUs).

Natural Language Processing and Speech

Learn the fundamental approaches to language modeling, and discuss applications such as machine translation, text generation, information extraction, and automatic summarization.

By the end of this course, learners will be able to:

  • Use machine learning methods for language modeling, part of speech tagging, and parsing.
  • Consider applications such as information extraction, machine translation, text generation, and automatic summarization.
  • Examine state-of-the-art neural network approaches to natural language processing.
  • Understand how tobuild automated systems that can analyze, understand, and produce language using industry standard tools like PyTorch and Hugging Face.

Computer Vision and Robotics

Computer vision forms the basis of the perception problem, while robots interact with the physical world via mechanisms. Examine how algorithms and learning integrate with physical systems. Cover ideas in image sensing, processing and filtering, segmentation, and object recognition. Perform planning and estimation for robotic systems.

Security, Privacy, Policy

Learn fundamental aspects of data security and privacy, explore how these play into technical tasks like data mining and storage, and examine the legal and social frameworks surrounding these issues. Study how all relate to policy development and solutions.

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

On-Campus Immersion Experience

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

Set to take place on Columbia’s Morningside campus in Manhattan, the immersion features working sessions, presentations, networking opportunities, and community-building events with peers, instructors, and alumni.

The first immersion is planned for September 2022. The dates for this in-person event will be shared in the coming months as we continue to get updates on public health and university guidance for large group meetings.  If public health guidance warrants it, the event may be delivered virtually.

International Visas

The online AI program from Columbia Engineering is a non-degree program. It does not grant eligibility for a student visa, and individuals are not permitted to study on visitor or visa waiver (ESTA) status. Therefore, international applicants wishing to attend the in-person NYC campus immersion are expected to have their own immigration status that permits them to live in or enter the United States, in accordance with the U.S. Department of State guidance. If you have any questions about visa requirements, please email the International Student and Scholar Office. You may also visit the U.S. Department of State Visa Information website for more information regarding visiting the U.S.

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Learn from world-class Columbia instructors, collaborate with cross-industry peers, and integrate AI into your organizational strategy.

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