👋 Quick question — how are we doing?

    Gradia
    Unsupervised Machine Learning: Unlocking the Potential of Data
    MIT Sloan School of Management

    Unsupervised Machine Learning: Unlocking the Potential of Data

    MIT Sloan School of Management, Cambridge
    HomeMIT Sloan School of ManagementUnsupervised Machine Learning: Unlocking the Potential of Data
    7 weeksDuration
    onlineFormat
    EnglishLanguage
    Data & AITopic

    Available Cohorts

    Choose your preferred start date

    Jun 10 - Jul 28, 2026
    7 weeks · online · Self-Paced
    Open
    Aug 19 - Oct 6, 2026
    7 weeks · online · Self-Paced
    Open
    Nov 11, 2026 - Jan 26, 2027
    11 weeks · online · Self-Paced
    Open
    $3,250

    All-inclusive program fee

    About This Program

    In business, data is only worth as much as the insights it provides, and insights rely completely on your ability to analyze and understand. Traditional artificial intelligence (AI) models require large annotated data sets leaving significant amounts of data untapped. This leaves valuable opportunities for your organization unrealized.


    In the Unsupervised Machine Learning: Unlocking the Potential of Data online short course from the MIT Sloan School of Management and the MIT Schwarzman College of Computing, you’ll tap into your organization’s data to create new value. Over six weeks, you’ll explore the technical and strategic aspects of unsupervised machine learning (ML) within your business context. By learning the approaches, capabilities, limitations, and applications of ML, you’ll be able to deploy AI solutions tailored to your organization’s goals, and leverage the insights of your previously unutilized data.


    Over six weeks, you’ll explore the business opportunities created by leveraging previously uncurated and unutilized data to train machine learning models. You’ll investigate how to build accurate AI models using representations, and how generative models can unlock the potential of your data. By exploring the landscape of pre-trained models, you’ll learn to create a strategy that ensures interpretability and causal inference in the deployment of ML in your organization. Finally, you’ll create a roadmap to ensure the models you’re employing are used in a responsible and maintenance-friendly manner.


    Leverage unutilized data: Redefine the potential of small or uncurated data using ML techniques.


    Create business value: Overcome business challenges and drive AI initiatives with representation learning and generative models.


    Apply your learnings: Understand the current landscape of pre-trained models and explore how these can be used to build different applications.

    Why MIT Sloan School of Management?

    MIT Sloan doesn't trade on prestige alone — it trades on proximity. Proximity to one of the world's densest concentrations of engineering, AI, and life sciences research, and to a faculty that publishes the ideas executives will be managing around in five years. If you want to understand how technology reshapes strategy before it reshapes your industry, this is the room to be in.

    Your Profile

    • The business value created by unsupervised ML makes the content of this program ideal for a range of professionals in decision-making positions. Managers and technology leads will learn how to uncover the potential of their uncurated data through planning new data acquisition protocols. IT and tech professionals who are searching for the most recent techniques and developments to build machine learning models will gain a deeper understanding of these niches. Anyone currently working in data science or data analytics will also improve their knowledge of computer vision technologies and their applications. 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

    Benefits

    • Leverage unutilized data: Redefine the potential of small or uncurated data using ML techniques.
    • Create business value: Overcome business challenges and drive AI initiatives with representation learning and generative models.
    • Apply your learnings: Understand the current landscape of pre-trained models and explore how these can be used to build different applications.

    What You'll Learn

    • The Potential of Data
    • Learning and Leveraging Representations
    • Generative Models
    • AI Building Blocks
    • Adapting AI Tools
    • Challenging and the Future