Students should understand AI concepts, ML algorithms such as decision trees, random forests, support vector machines, neural networks as well as reinforcement and encapsulation techniques. Students should compare and contrast ML algorithms that differ in accuracy, linearity, and number of parameters. This subject aims to provide students with the knowledge and skills to apply AI and ML to the career of a financial analyst.
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