Completed notes of the course

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From ML to DL ML paradigms Machine Learning is the field of study focused on the development of algorithms and statistical models able to perform tasks not being specifically programmed to solve it, but to learn the optimal procedure to obtain the best outcomes. Machine learning algorithms usually consist of data-driven models that are designed to be fed with hand- crafted feature vectors and to make predictions or decisions based on them. Those feature vectors, in ML approaches, consist of structured datasets containing features that are extracted from raw data, usually by experts in the field (HAND-CRAFTED FEATURES). On the other hand, in DL approaches, also the feature extractor is data-driven, thus designed to be learned from data. Hand-crafted vs Data-driven Hand-crafted features may be more convenient than data-driven feature extraction in situations where the characteristics of the data are well-understood, and the relevant information can be easily identified and extracted by domain experts. In this case, the use of hand-crafted features in combination with ML/statistical models result to: ∟ be more efficient (during training), less expensive in terms of resources (time, computational power) but performing even with limited training data available ∟ be interpretable and adjustable (when you know what the model is using to make prediction and how, you can give more/less relevance to some features to adjust the output) ∟ exploit the expert’s knowledge ∟ require more design/programming efforts ∟ be not general and portable, what means that they may not perform well or be useful on other data sets or in other contexts. This is because the extracted features are usually very tailored to the specific problem/case, with respect to features learned from data that in…