Shortcuts | CSCI-UA. 473 Syllabus | CSCI-UA. 473 Notes | CSCI-GA. 2566 Syllabus | CSCI-GA. 2566 Slides
This is a series of undergraduate-graduate-level courses at the Courant Institute of Mathematical Sciences, NYU.
Course information
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Instructor: Joan Bruna & Mehryar Mohri
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Semester: Fall 2024
- Outline:
- CSCI-UA. 473: ML Paradigms, Least Square Problems, Supervised Learning, Universal Approximation Theorems, Curse of Dimensionality, Optimizations(GD,SGD), Geometric Deep Learning
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CSCI-GA. 2566: Probability tools, concentration inequalities; PAC model; Rademacher complexity, growth function, VC-dimension; Perceptron, Winnow; Support vector machines (SVMs); Kernel methods; Boosting; On-line learning; Decision trees; Density estimation, maximum entropy models; Logistic regression, conditional maximum entropy models; Regression problems and algorithms; Ranking problems and algorithms; Learning languages and automata; Reinforcement learning, Markov decision processes(MDPs)
- Textbook: The Elements of Statistical Learning - Data Mining, Inference, and Prediction [2nd Edition] (Trevor Hastie, et al.), Foundations of Machine Learning [2nd Edition] (Mehryar Mohri, et al.)
Gradebook
Overall grade: A (3.00/3.00)