EE 270
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EE 270 - Large Scale Matrix Computation, Optimization and Learning
Lecture slides
Lecture 1: Introduction and overview of topics
Lecture 2: Approximate Matrix Multiplication (AMM)
Lecture 3: AMM Applications, Error Analysis, Trace Estimation and Bootstrap
Lecture 4: Approximate Tensor Products, Randomized Verification and Concentration Inequalities
Lecture 5: Randomized Dimension Reduction and Johnson Lindenstrauss (JL) lemma
Lecture 6: Applications of JL Embeddings
Lecture 7: Least Squares Problems and Random Projections
Lecture 8: Randomized Least Squares Solvers, Bias and Variance, Streaming Data
Lecture 9: High-dimensional Problems, Least-norm Solutions and Randomized Methods
Lecture 10: Leverage Scores and Basic Inequality Method
Lecture 11: Spectral Approximation, Subspace Embedding and Fast JL Transforms
Lecture 12: Gradient Descent and Convex Functions
Lecture 13: Gradient Descent with Momentum, Condition Numbers and Lyapunov Analysis
Lecture 14: Second-Order Optimization Algorithms, Strong Convexity and Randomized Preconditioners
Lecture 15: Randomized Newton’s Method and Logarithmic Barrier
Lecture 16: Stochastic Gradient Methods and Randomized Kaczmarz Algorithm
Lecture 17: Randomized Singular Value Decomposition and CX Decomposition
Lecture 18: Generalized Least Squares Problems, Randomized Low Rank Approximations and Power Iteration
Lecture 19: Kernel Matrices, Effective Dimension, Nystrom Method and Random Fourier Features
Lecture 20: Determinantal Point Processes and Markov Chain Monte Carlo