I was a teaching assistant in the course optimization for machine learning. I gave lectures on optimality condtions, convex functions and optimization. The course focused on optimization methods that are suitable for large-scale problems arising in data science and machine learning applications. I explored several algorithms that are efficient for both smooth and nonsmooth problems as well as distributed optimization.
I was the teaching assistant, and gave lectures in the course pattern recognition, offered by Machine Learning and Computation Modeling Lab. Much of the topics concern statistical classification methods. They include generative methods such as those based on Bayes decision theory and related techniques of parameter estimation and density estimation. Next come discriminative methods such as nearest-neighbor classification, support vector machines. Artificial neural networks, classifier combination and clustering are other major components of pattern recognition.