Explore the power of generalized tensor decomposition for data analysis in this 57-minute webinar. Dive into unsupervised learning methodologies applicable to chemometrics, criminology, and neuroscience. Learn about low-rank tensor decomposition using canonical polyadic or CANDECOMP/PARAFAC format, and understand the limitations of the standard sum of squares error (SSE) metric. Discover alternative objective functions like KL divergence for count data, logistic odds for binary data, and beta-divergence for nonnegative data. Examine real-world applications, including a detailed analysis of Chicago Crime Data from 2019. Explore computational aspects of generalized tensor decomposition, current research, and open challenges in the field. Follow along with topics such as matrix decomposition, CP optimization problems, probabilistic interpretations, and stochastic sampling methods. Gain insights into the utility of these techniques for complex data analysis across various domains.