For my first week, I mainly worked through tutorials essential for understanding the project I am working on. These tutorials included data visualization, introductory machine learning material, and temporal data handling. From there, I read introductory articles on reservoir computing which is a key part of the project that I am working on. I learned that reservoir computing is a type of recurrent neural network (RNN). Reservoir computing components include an input, a reservoir (the random, recurrently connected nodes), a readout layer, and a final output layer. Unlike a typical RNN in which you have to train all the weights in the network which then requires the use of backpropagation, reservoir computing only trains the weights between the readout and final output layer. Only training the readout weights means that the reservoir is left completely untrained its weights are typical set to random values. The use of a reservoir makes training much easier and more flexible.