MIT Sloan and MIT Schwarzman College

Unsupervised Machine Learning: Unlocking the Potential of Data

Reimagine the untapped potential of your underutilized data by employing unsupervised machine learning techniques.

Start date

19 March 2025

Price

$2,950 USD

Installment Plan

Request a call

Course duration

6 weeks

Excluding orientation

Language

English

Access resources from start date

Effort

6–8 hours per week

Self-paced learning online

ABOUT THIS COURSE

Course overview

70%

The number of organizations that will shift focus from big to small and wide data by 2025.

Gartner (May, 2021).

56%

The percentage of organizations already adopting AI in a minimum of one function.

McKinsey (Dec, 2021).

Duration: 6 weeks (excluding orientation)

Traditionally, the process of training artificial intelligence (AI) models involved using large sets of annotated data. Consequently, unorganized and limited datasets that hold immense value are often disregarded. However, with the latest machine learning (ML) developments, businesses can leverage these untapped data sources to gain a competitive edge.

The Unsupervised Machine Learning: Unlocking the Potential of Data online short course, offered by the MIT Sloan School of Management and the MIT Schwarzman College of Computing, delves into the technical and strategic aspects of unsupervised learning techniques. Over six weeks, you’ll explore these approaches, their capabilities, limitations, use cases, and practical applications. Through this course, you’ll realize the latent potential of your data, regardless of its size or quality, and develop customized AI solutions that cater to your unique business challenges.

Is this course for you?

This program aims to assist business leaders in capitalizing on the possibilities presented by unsupervised machine learning. Technology managers and decision makers can formulate strategies for acquiring new data by gaining knowledge on how to utilize unstructured data. The program material will be valuable for IT and tech experts seeking the latest advancements in unsupervised learning and data analysts and scientists seeking to enhance their expertise in utilizing computer vision technologies.

The ability to code is not a prerequisite for this program. However, this program contains complex concepts that require an existing understanding and above-average knowledge of machine learning.

Course overview

Duration: 6 weeks (excluding orientation)

The ability to code is not a prerequisite for this program. However, this program contains complex concepts that require an existing understanding and above-average knowledge of machine learning.

What will set you apart

On completion of this course, you’ll walk away with:

The ability to harness previously unused data within your organization to deploy ML models.

The technical foundation needed to address business challenges and drive AI initiatives through representation learning.

An understanding of the challenges, opportunities, and important considerations of generative models.

The skills to evaluate and interact with existing pre-trained models and leverage these effectively in your organization.

The skills required to build transparent and interpretable ML models in your context.

Course curriculum

Unearth the potential of unsupervised machine learning as you work through the weekly modules of this online short course.

Orientation module

Welcome to your Online Campus

Module 1

The potential of data

Module 2

Learning and leveraging representations

Module 3

Generative models

Module 4

AI building blocks

Module 5

Adapting AI tools

Module 6

Challenges and the future

Faculty Directors

The design of this online short course is guided by MIT Sloan and MIT Schwarzman College faculty, as well as MIT guests and industry experts, who will share their experience and in-depth knowledge with you throughout the course. View the brochure to see all the industry thought leaders featured in this course.

Who you'll learn from

Antonio Torralba
Delta Electronics Professor of Electrical Engineering and Computer Science, Head of AI+D Faculty, EECS Department, MIT CSAIL

Vivek Farias
Patrick J. McGovern (1959) Professor, Professor of Operations Management, MIT Sloan School of Management

Phillip Isola
Associate Professor in EECS, MIT

Jacob Andreas
Assistant Professor, MIT CSAIL

Rama Ramakrishnan
Professor of the Practice, Data Science and Applied Machine Learning, MIT Sloan School of Management

About the certificate

Acquire the knowledge to leverage your data for building AI systems and earn an official certificate of completion from the MIT Sloan School of Management.

This program also counts toward an MIT Sloan Executive Certificate, which you can earn upon completion of four programs where at least three of the four come from your chosen certificate track and at least one is completed in person. Find full details here.

Your certificate will be issued in your legal name and sent to you digitally upon successful completion of the program, as per the stipulated requirements.

MIT Sloan Unsupervised Machine Learning: Unlocking the Potential of Data online program certificate

YOUR INFORMATION

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Answer a few questions and get more information about the course.

FAQ

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