Specialization: Machine LearningInstructor: Geena Kim, Assistant Teaching ProfessorPrior knowledge needed: Calculus, Linear algebra, PythonLearning Outcomes Explain what unsupervised learning is, and ...

Another goal is reducing the computing cost when detecting the optimal feature subset of very high dimensionality data, such as SNP data. More information: Mingzhao Wang et al, Unsupervised spectral ...

Abstract: Owing to the generation of vast amount of unlabelled dynamic data and the need to analyze them, deep unsupervised learning based clustering algorithms are gaining importance in the field of ...

The Certificate in Data Science provides you with the knowledge to draw conclusions on data reliably and robustly. ... unsupervised learning or clustering (the K-means family, co-clustering, mixture ...

Describe the concept of topic modeling and related terminology (e.g., unsupervised machine learning) Apply topic modeling to marketing data via a peer-graded project; Apply topic modeling to a variety ...

Partially as a response to the challenges of multiscale dynamics, machine learning has emerged as a leading method for analysis. However, machine learning has clear drawbacks in terms of computational ...

Malaya Rout works as Director of Data Science with Exafluence in Chennai. He is an alumnus of IIM Calcutta. He has worked with TCS, LatentView Analytics and Verizon prior to the role at Exafluence ...