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University of Colorado Boulder

Unsupervised Algorithms in Machine Learning

University of Colorado Boulder via Coursera

Overview

One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms.

Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. College-level math skills, including Calculus and Linear Algebra, are needed. It is recommended, but not required, to take the first course in the specialization, Introduction to Machine Learning: Supervised Learning.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:

MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder


Course logo image by Ryan Wallace on Unsplash.

Syllabus

  • Unsupervised Learning Intro
    • Now that you have a solid foundation in Supervised Learning, we shift our attention to uncovering the hidden structure from unlabeled data. We will start with an introduction to Unsupervised Learning. In this course, the models no longer have labels to learn from. They need to make sense of the data from the observations themselves. This week we are diving into Principal Component Analysis, PCA, a foundational dimension reduction technique. When you first start learning this topic, it might not seem easy. There is undoubtedly some math involved in this section. However, PCA can be grasped conceptually, perhaps more readily than anticipated. In the Supervised Learning course, we struggled with the Curse of Dimensionality. This week, we will see how PCA can reduce the number of dimensions and improve classification/regression tasks. You will have reading, a quiz, and a Jupyter notebook lab/Peer Review to implement the PCA algorithm.
  • Clustering
    • This week, we are working with clustering, one of the most popular unsupervised learning methods. Last week, we used PCA to find a low-dimensional representation of data. Clustering, on the other hand, finds subgroups among observations. We can get a meaningful intuition of the data structure or use a procedure like Cluster-then-predict. Clustering has several applications ranging from marketing customer segmentation and advertising, identifying similar movies/music, to genomics research and disease subtypes discovery. We will focus our efforts mainly on K-means clustering and hierarchical clustering with consideration to the benefits and disadvantages of both and the choice of metrics like distance or linkage. We have reading, a quiz, and a Jupyter notebook lab/Peer Review this week.
  • Recommender System
    • This week we are working with Recommender Systems. Websites like Netflix, Amazon, and YouTube will surface personalized recommendations for movies, items, or videos. This week, we explore Recommendation Engines' strategies to predict users' likes. We will consider popularity, content-based, and collaborative filtering approaches, and what similarity metrics to use. As we work with Recommendation Systems, there are challenges, like the time complexity of operations and sparse data. This week is relatively math dense. You will have a quiz wherein you will work with different similarity metric calculations. Give yourself time for this week's Jupyter notebook lab and consider performant implementations. The Peer Review section this week is short.
  • Matrix Factorization
    • We are already at the last week of course material! Get ready for another dense math week. Last week, we learned about Recommendation Systems. We used a Neighborhood Method of Collaborative Filtering, utilizing similarity measures. Latent Factor Models, including the popular Matrix Factorization (MF), can also be used for Collaborative Filtering. A 1999 publication in Nature made Non-negative Matrix Factorization extremely popular. MF has many applications, including image analysis, text mining/topic modeling, Recommender systems, audio signal separation, analytic chemistry, and gene expression analysis. For this week, we focus on Singular Value Decomposition, Non-negative Matrix Factorization, and Approximation methods. This week, we have reading, a quiz, and a Kaggle mini-project utilizing matrix factorization to categorize news articles.

Taught by

Geena Kim

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