This is part of a two-course sequence on Large Scale Optimization and Large Scale Learning. We focus on Convex Optimization including basic material from convex geometry, convex analysis and convex optimization. It will cover basic modeling, and understanding how to find and exploit convexity, both for theoretical analysis, and also for developing algorithms. This class is structured to be interesting and relevant to students who are using or plan to use optimization in their research, and are interested in solving large-scale optimization problems. The target audience is quite broad: graduate students from ECE, CS, OR, Math, DSSC, and related disciplines.