Deep Learning

CT Score

1500

#0 of 0 in COMP

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In the tree

1. Proficiency in Python 3. 2. Familiarity with fundamental concepts of calculus, including partial derivatives, chain rule, total derivatives, derivatives and partial derivatives of vectors and matrices. 3. Familiarity with fundamental concepts of probability and statistics, including probability distributions, density functions, computing probabilities, expectation, variance, multivariate distributions, random variables and multivariate random variables. 4. Familiarity with fundamental concepts of linear algebra, such as inner products, vector spaces, vector and matrix norms, rank of a matrix, positive definite matrices, and matrix factorization, e.g., spectral decomposition and singular value decomposition. 5. Familiarity with fundamental concepts of machine learning and optimization theory, such as loss functions, gradient descent, maximum likelihood estimation, MAP estimation, and principal component analysis. Mutually Exclusive: Cannot register for COMP 647 if student has credit for COMP 646 / ELEC 576→COMP 647→unlocks 0 courses

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