Geoscientists have used machine learning for at least three decades and the applications spam many fields, from seismic processing and interpretation, to remote sensing classification, to analysis of well log data, among many others. More popular in some fields (e.g. seismic interpretation, remote sensing analysis) than others (e.g. paleontology), machine learning tools can leverage research in different areas of geoscience. Although machine learning is becoming more popular in different fields of geoscience, some concepts of more modern applications, convolutional neural networks in particular, are still vaguely understood by non-practitioners. I present some of the key concepts of machine learning with more details on the foundations of convolutional neural networks and some techniques that can help better understand convolutional neural networks behavior. I then present five case studies, mostly using convolutional neural networks and transfer learning. Transfer learning is a methodology that allow us to repurpose filters created by convolutional neural networks on a primary task to perform a secondary task. The five case studies start with a broader application of convolutional neural networks for different geoscience images, including thin-sections and core photographs. Then I present a how to perform core classification using convolutional neural networks. Next, how microfossils can be classified by the same methodology. I present a more detailed analysis of transfer learning using different remote sensing datasets. In the final case study, I show applications of supervised learning techniques to help forecast Megaelectron-Volt electrons inside Earthβs outer radiation belt. I conclude the dissertation with a summary and comments on the expectation of future research
