EEE465 Numerical Methods for Artificial Intelligence

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lecture notes from: Matrix Methods in Machine Learning, Laurent Lessard

DERS UYGULAMA BELGESİ (SYLLABUS)

weekly schedule

week part topic
1 linear algebra introduction; linear algebra review; block matrices and norms
2 linear algebra linear independence and rank; subspaces and linear equations
3 least squares least squares (LS); vector derivatives and positive semidefinite matrices
4 least squares orthogonality and Gram-Schmidt algorithm; LS classification and cross-validation
5 singular value decomposition motivating the singular value decomposition (SVD); matrix norms and the SVD
6 singular value decomposition SVD geometry and principal component analysis; low-rank approximation and pseudoinverse; SVD geometry and sensitivity
7   review and preparation for the exam (midterm)
8   midterm exam
9 intro to optimization convex optimization; trade-offs and regularization; regularization examples
10 optimization for machine learning iterative methods; proximal algorithms; convexity and support vector machine (SVM)
11 optimization for machine learning gradient methods; stochastic gradient method; max-margin SVM and kernels
12 constrained optimization bounding and duality; Karush-Kuhn-Tucker conditions
13 machine learning artificial neural networks and deep learning; convolutional neural networks
14 machine learning unsupervised learning and k-means clustering; matrix problems and vectorization
15   review and preparation for the exam (final)

resources

lecture videos

Matrix Methods in Machine Learning, Laurent Lessard

textbooks (linear algebra)

Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares (Boyd&Vandenberghe)

Linear Algebra for Everyone (Strang)

Linear Algebra and Learning from Data (Strang)

textbooks (optimization)

An Introduction to Optimization (Chong&Zak)

Constrained Optimization and Lagrange Multiplier Methods (Bertsekas)

Convex Optimization: Algorithms and Complexity (Bubeck)

Convex Optimization (Boyd&Vandenberghe)

textbooks (machine learning)

Matrix Methods in Data Mining and Pattern Recognition (Eldén)

Learning from Data (Abu-Mostafa&Magdon-Ismail&Lin)

Mathematics for Machine Learning (Deisenroth&Faisal&Ong)

Deep Learning (Goodfellow&Bengio&Courville)

Neural Networks and Deep Learning (Nielsen)

Youtube playlists

Introduction to Applied Linear Algebra (Stephen Boyd)

Linear Algebra (Gilbert Strang)

Essence of Linear Algebra (3Blue1Brown)

Convex Optimization (Visually Explained)

Intro to Data Science (and Machine Learning) (Steve Brunton)

Introduction to Statistics and Data Analysis (Steve Brunton)

Reinforcement Learning (Steve Brunton)

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