COURSE DESCRIPTION

Within Machine Learning problems, those of unsupervised learning such as Clustering and Retrieval remain the hardest to resolve and likewise one of the most flexible and useful tools a Machine Learning practitioner could possess in their arsenal. This course concerns extracting valuable information from seemingly unorganized and unlabeled data, which often exists in vast quantities and remains unused otherwise. It is often used as either an analytic tool to aid data scientists or an auxiliary tool to help supervised processes achieve better results.

LEARNING OUTCOMES

• Understand the general idea of Clustering and Retrieval
• Understand Nearest Neighbor Search Algorithms
• Understand K-means Algorithm and Understand how it works
• Understand Mixture Models Idea
• Understand a combined way between Mixed Membership Modelling and Latent Dirichlet Allocation
• Understand another approach in the Clustering problem