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Brown University

Machine Learning: Unsupervised Learning

Brown University and Georgia Institute of Technology via Udacity

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Overview

Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

Syllabus

  • Clustering
    • Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
  • Hierarchical and Density-Based Clustering
    • We continue to look at clustering methods. Here, we'll discuss hierarchical clustering and density-based clustering (DBSCAN).
  • Gaussian Mixture Models and Cluster Validation
    • In this lesson, we discuss Gaussian mixture model clustering. We then talk about the cluster analysis process and how to validate clustering results.
  • Dimensionality Reduction and PCA
    • Often we need to reduce a large number of features in our data to a smaller, more relevant set. Principal Component Analysis, or PCA, is a method of feature extraction and dimensionality reduction.
  • Random Projection and ICA
    • In this lesson, we will look at two other methods for feature extraction and dimensionality reduction: Random Projection and Independent Component Analysis (ICA).
  • Project: Identify Customer Segments
    • In this project, you'll apply your unsupervised learning skills to two demographics datasets, to identify segments and clusters in the population, and see how customers of a company map to them.

Taught by

Charles Isbell and Michael Littman

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Reviews

3.3 rating, based on 3 Class Central reviews

Start your review of Machine Learning: Unsupervised Learning

  • Vinayak Mehta
    27
    The way in which the instructors teach is awesome.

    This is a masters level machine learning course. I would recommend taking this course at a slow pace if you're a beginner in the machine learning domain, making sure that you get a thorough understanding of the material.
  • Anonymous
    Michael is terrible teacher. His descriptions of ML algorithms are awful and so is his voice. I would not recommend this.
  • Michael
    1
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