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In ML, there are a variety of algorithms that can help solve problems. In graduate school, a course in AI will usually have a quick review of the core ML concepts (covered in a previous course) and then cover searching algorithms, game theory, Bayesian Networks, Markov Decision Processes (MDP), reinforcement learning, and more.
Data Science Knowing the ins and outs of data science encompasses the ability to handle, analyze, and interpret data, which is required for training models and understanding their outputs. GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN.
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