In this chapter, we discussed the concepts behind unsupervised and semi-supervised machine learning, and their Bayesian treatment. We learned two important Bayesian unsupervised models: the Bayesian mixture model and LDA. We discussed in detail the bgmm package for the Bayesian mixture model, and the topicmodels and lda packages for topic modeling. Since the subject of unsupervised learning is vast, we could only cover a few Bayesian methods in this chapter, just to give a flavor of the subject. We have not covered semi-supervised methods using both item labeling and feature labeling. Interested readers should refer to more specialized books in this subject. In the next chapter, we will learn another important class of models, namely neural networks.
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Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Read more about Hari Manassery Koduvely
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Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
Read more about Hari Manassery Koduvely