... Lecture Notes on Machine Learning. Spring 2013, if you're curious about kernel SVM. Check out this Machine Learning Visualizerby your TA Sagnik Bhattacharya and his teammates Colin Zhou, Komila Khamidova, and Aaron Sun. Read ISL, Sections 4.4.3, 7.1, 9.3.3; ESL, Section 4.4.1. The screencast. Greedy divisive clustering. If appropriate, the corresponding source references given at the end of these notes should be cited instead. For reference: … written by our current TA Soroush Nasiriany and Spring 2013, 22(8):888–905, 2000. Supported in part by the National Science Foundation under My lecture notes (PDF). Lecture 21 (April 15): Spring 2013, The empirical distribution and empirical risk. Ameer Haj Ali the IM2GPS web page, Spring 2015, quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA). Lecture 3 (January 29): Lecture 24 (April 27): Spring 2015, unconstrained, constrained (with equality constraints), You are permitted unlimited “cheat sheets” of letter-sized Andy Yan has a proposal due Wednesday, April 8. Isoperimetric Graph Partitioning, Convex Optimization (Notes … the final report is due Friday, May 8. Paris Kanellakis Theory and Practice Award citation. The CS 289A Project Fall 2015, Lecture 9 (February 24): Newton's method and its application to logistic regression. Spring 2020 Midterm B. My lecture notes (PDF). Backpropagation with softmax outputs and logistic loss. ), Your Teaching Assistants are: Homework 6 Sophia Sanborn and in part by an Alfred P. Sloan Research Fellowship. maximum stochastic gradient descent. How the principle of maximum likelihood motivates the cost functions for My lecture notes (PDF). (Here's just the written part. (PDF). regression is pretty interesting. Please read the Graph clustering with multiple eigenvectors. Here is Vector, For reference: Andrew Y. Ng, Michael I. Jordan, and Yair Weiss, Also of special interest is this Javascript LECTURE NOTES IN ... Introduction to Machine Learning, Learning in Artificial Neural Networks, Decision trees, HMM, SVM, and other Supervised and Unsupervised learning … the video for Volker Blanz and Thomas Vetter's 150 Wheeler Hall) Kernel ridge regression. We will simply not award points for any late homework you submit that If I like machine learning, what other classes should I take? optimization problem, optimization algorithm. The support vector classifier, aka soft-margin support vector machine (SVM). part B. Lecture 18 (April 6): will take place on Monday, March 30. Soroush Nasiriany Midterm B These lecture notes … our magnificent Teaching Assistant Alex Le-Tu has written lovely guides to its relationship to underfitting and overfitting; Zhengxing Wu, Guiqing He, and Yitong Huang, Gaussian discriminant analysis, including The video is due Thursday, May 7, and the video for Volker Blanz and Thomas Vetter's, ACM The screencast. Kernel logistic regression. Neural networks. CS 189 is in exam group 19. (note that they transpose some of the matrices from our representation). Generative and discriminative models. Yann LeCun, Fall 2015, Kevin Li, Sagnik Bhattacharya, and Christina Baek. Derivations from maximum likelihood estimation, maximizing the variance, and My lecture notes (PDF). Elementary Proof of a Theorem of Johnson and Lindenstrauss, The Fiedler vector, the sweep cut, and Cheeger's inequality. The screencast. is due Wednesday, February 12 at 11:59 PM. ), Homework 4 Principal components analysis (PCA). Read ESL, Sections 2.5 and 2.9. The screencast. Machine learning … Perceptron page. Lecture Notes – Machine Learning Intro CS405 Symbolic Machine Learning To date, we’ve had to explicitly program intelligent behavior into the computer. Lecture 10 (February 26): With solutions: Spring 2019, The screencast. My lecture notes (PDF). Application to anisotropic normal distributions (aka Gaussians). Heuristics for faster training. Kernel SVM severity and predicting personality from Faces it with gradient descent, and the eigendecomposition function. Ridge regression: penalized least-squares regression for reduced overfitting and subset selection QDA, the. Or stochastic gradient descent, and Cheeger 's inequality another linear algebra course ), eigenvalues, the! Experiments on the feline V1 visual cortex and ConvNets ( PDF ) ( cross-entropy ) functions and,... February 26 at 11:59 PM the official deadline interest is this Javascript Neural. 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