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This course will cover the principles of neuromorphic computing. Topics will cover 1) fundamentals of spiking neural network (SNN); 2) supervised and unsupervised learning algorithms for SNN; 3) novel applications of SNN, including in vision and time series processing; 4) architectures for implementing SNN in hardware; 5) introduction to non-volatile memory technologies to implement synaptic processing in neuromorphic hardware; 6) software stacks for neuromorphic computing; and 7) design challenges in dependable neuromorphic computing. Software projects in this course will involve developing SNN models and training algorithms in PyTorch, a Python based machine learning framework. Hardware projects will be based on Verilog HDL and prototyped on FPGA. Familiarity with PyTorch and/or Verilog is required.