Education
Information about some courseworks
Collective Decision Making
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An intersting graduate course on Collective Decision Making, topics like Byzantine agreement, Computations social choice, Mechanism Design, etc.
Computational Geomerty
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Graduate course on computational Geometry.
Probabilistic Graphical Models
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This graduate level course is based on Koller’s PGMs book. In representation part, inference part, and learning part of the book, the have learned mostly chapter 3-5, 9-13, and 17-18 respectively. Through this course, we have learned graphical representation and its properties, ways to estimate posterior distribution in reasonable time (e.g. MCMC), and how to predict parameters and graphical structures. I have audited this course.
Statistical Machine Learning
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This graduate level course is based on Wasserman’s All of The Statistics book. The first 14 Chapters of the book were covered. We have learned topics like: Different types of convergence, Parametric and Non Parametric learning, Bootstrap technique for finding confidence interval, parameter inference and model selections. I have audited this course.
Introduction to Data Mining
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In this course, we have learned different techniques to use data to for prediction. We have learned regression, decision trees, enrtopy concepts, perceptron, evalutaion metrics like ROC, and clustering techniques.
Topics in Computer Science (Algorithm II)
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In this course we have been introduced to Computational Geometry, Linear Programming, Online Algorithms, Approximation Algorithms, Some NP complete examples.
An introduction to Stochastic Process
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In this course we have learned about random walk, some properties of a continues time and discrete time markov chains and their stationary states.