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This working document is the Focus Group's report, containing the study materials and other learning resources collected by the Focus Group members organized into different sessions and subsections. This document is not an extensive survey of the available study materials, and we do not claim that this document list lists all the available study materials or resources. The Focus Group met every two weeks, discussed the materials collected till then, and started documenting these here.
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ML algorithms can be broadly classified into Traditional Machine Learning and Deep Learning (DL). These are classified into Supervised or Semi-Supervised Learning, Unsupervised Learning, and Reinforcement Learning. DL uses Artificial Neural Networks (ANN) to learn data representation, while Traditional ML techniques use non-ANN-based frameworks. Supervised or Semi-Supervised Learning uses pre-labeled data to provide "examples" for from which the ML algorithm to can learn from. In Unsupervised Learning, the ML algorithms are provided with unlabelled data. In Reinforcement Learning, a feedback loop provides inputs to ML algorithms about how well the algorithm performs on any given data item. This feedback loop constantly improves the system as more data is available.
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