So Thomas Henson and Erin K. Banks combined weeks 7 – 11 for one quick review of the Coursera Machine Learning class with Andrew Ng. (If you have not started with the week 1 video… you probably should). Now we couldn’t completely review all the weeks because… well… someone in the video didn’t actually finish the course on time. Which is fine considering that as Thomas likes to say… “life got in the way”… or he got a new job. Congrats Thomas!!
For a quick recap of the weeks, watch the video but the following is the syllabus:
Support Vector Machines – Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.
Unsupervised Learning – We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.
Dimensionality Reduction – In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.
Anomaly Detection – Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
Recommender Systems – When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.
Large Scale Machine Learning – Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.
Application Example: Photo OCR – Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.
Learn more about Coursera’s Machine Learning – Andrew Ng week 7-11 and how Thomas and Erin did in this week’s video below.