| Abstract: Modern instrumentation and laboratory techniques have resulted in an exponential rise in the volume and complexity of data collected for astrobiological research. As such, a shift from standard statistical approaches to more advanced statistical techniques is necessary to adequately understand and examine these more complex datasets. Currently, machine learning methods are underutilized in astrobiology; however, such techniques excel at revealing structure and hidden patterns in large and/or complex data. Machine learning can be daunting to implement in astrobiology work due to the plethora of algorithms available, a requirement of some programming knowledge, and data science and statistical literacy. This talk will provide the basics and fundamentals of artificial intelligence (AI) and machine learning (ML) to equip astrobiologists with a foundation for implementing available AI/ML techniques in their own research. More specifically, Part 1 of this series will focus on the fundamentals and applications of unsupervised learning techniques. Additionally, I will provide a publicly available python notebook tutorial designed to teach and introduce machine learning approaches including unsupervised and supervised methods in the context of astrobiology.
Watch Part 1: Unsupervised Learning | 
