In this lecture, we continue our investigation of Artificial Neural Networks (ANN), but we move from supervised learning through reinforcement learning (RL) to an introduction to unsupervised learning. We pick up where we left off with reservoir machines (e.g., Echo-State Networks, ESN's), which reduce the amount of training necessary for a complex supervised learning task. We then pivot to talk about reinforcement learning and introduce Q-learning (specifically deep Q-learning). We then close with an introduction to unsupervised learning, with a brief discussion of clustering, anomaly detection, and computational creativity provided by generative adversarial networks (GAN). This will set us up for multidimensional scaling (MDS) in the next lecture and a march toward Hebbian learning, spike-time-dependent plasticity, and spiking neural networks (SNN's).
Whiteboard notes for this lecture can be found at:
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