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APRIL 2024 I SMT007 MAGAZINE 13 dict outcomes without explicit programming and incorporate intelligence into a machine by automatically learning from the data. A learn- ing algorithm then trains a model to generate a prediction for the response to new data or the test datasets. ere are three types of ML: supervised, unsupervised, and reinforcement. • Supervised ML is task-driven and requires a data analyst to provide input and a desired output, then determine which variables the model should analyze • Unsupervised ML is data-driven and does not have labeled data. Its focus is learning more about the data by inferring patterns in the dataset without reference to the known outputs. • Reinforcement learning uses algorithms that learn from outcomes and decide which action to take next. In reinforce- ment learning, there is no data input, or desired output but the reinforcement agent decides what to do to perform the given task by learning from its experience with a trial-and-error method to achieve the maximum reward in an environment. An agent learns to make decisions by inter- acting with an environment and receives feedback in the form of rewards or penal- ties based on the actions it takes. In addition to these basic ML techniques, more advanced ML approaches continue to emerge. ML understands patterns and can instantly see anomalies that fall outside those patterns, making it a valuable tool in myriad applica- tions, ranging from fraud detection and cyber threat detection to manufacturing and supply chain operation. Deep Learning Deep learning is a subset of machine learning based on multi-layered neural networks that learn from vast amounts of data. It comprises a series of algorithms trained and run on deep neural networks that mimic the human brain to incorporate intelligence into a machine. Most deep learning methods use neural net- work architectures, so they are oen referred to as deep neural networks. Soware architec- ture (type, number, and organization of the layers) is built empirically following an intu- Figure 1.