Understanding 10 701 Machine Learning Fall 2014 Lecture 21
Exploring 10 701 Machine Learning Fall 2014 Lecture 21 reveals several interesting facts. Topics: expectation maximization (EM), convergence of EM, principal component analysis (PCA)
Key Takeaways about 10 701 Machine Learning Fall 2014 Lecture 21
- Topics: course logistics, high-level overview of
- Topics: overview of topics that may tested on exam, open Q&A
- Topics: principal component analysis (PCA), deep
- Topics: perceptron, linear programming, "perceptron algorithm"
- Topics: kernel perceptron, kernel engineering, support vector
Detailed Analysis of 10 701 Machine Learning Fall 2014 Lecture 21
Topics: error bounds for infinite hypothesis spaces, Vapnik–Chervonenkis (VC) dimension, Rademacher complexity Topics: overview of topics tested on exam, Q&A CMU 2015
Topics: Newton's method, backtracking line search, constrained optimization, stochastic gradient descent, density estimation ...
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