Algorithms in Machine Learning (Fall 2020)
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Optimization in Machine Learning
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Bayesian Machine Learning and Information Processing (Fall 2020)
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Algorithms (Winter 2020)
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Bandit Algorithms and Online Learning (Fall 2021)
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Advanced Algorithms (Fall 2021)
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Data Systems for Machine Learning (Fall 2020)
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Classical Mechanics (Fall 2020)
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General Topology (Fall 2020)
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Computer Networks (Fall 2020)
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Machine Learning (Fall 2019)
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Measure Theory (Fall 2020)
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Software Design (Fall 2020)
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Course on the Statistical Learning Theory (Fall 2020)
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Machine Learning Theory (Spring 2021)
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Introduction to Machine Learning (Fall 2020)
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Matrix Theory (Fall 2020)
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Analysis 1 (Fall 2020)
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Multivariate Statistics and Machine Learning (Fall 2020)
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Applied Numerical Algorithms (Fall 2023)
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Introduction to Category Theory (Fall 2020)
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Algorithms for Data Science (Spring 2021)
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Introduction to Optimization (Fall 2020)
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Computational Complexity (Fall 2020)
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Matrix Calculus For Machine Learning And Beyond (IAP 2023)
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Topics in Real Analysis (Fall 2020)
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Monte Carlo Methods (Fall 2020)
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Introduction to Graph Theory (Fall 2020)
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Intro to Data Structures and Algorithms (Fall 2020)
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Differential Forms (Fall 2020)
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Network Optimization (Fall 2018)
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Probability and Info Theory in Machine Learning (Fall 2020)
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Abstract Algebra (Fall 2020)
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Graph Theory (Fall 2020)
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Combinatorial Optimization (Fall 2020)
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Symmetry in Physics (Fall 2020)
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Topics in Analysis: Descriptive Set Theory (Fall 2022)
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Graph Algorithms (Winter 2021)
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Numerical Methods (Fall 2020)
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Martingale Theory with Applications (Fall 2020)
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Abstract Linear Algebra (Fall 2021)