Wheeler Hall Auditorium (a.k.a. Kara Liu unlimited blank scrap paper. The exhaustive algorithm for k-nearest neighbor queries. Spring 2020. its relationship to underfitting and overfitting; The screencast. Ensemble learning: bagging (bootstrap aggregating), random forests. The screencast. My lecture notes (PDF). Hardcover and eTextbook versions are also available. datasets Shewchuk discussion sections related to those topics. The Machine Learning Approach • Instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A Morphable Model for the Synthesis of 3D Faces. Heuristics for avoiding bad local minima. Hermish Mehta fine short discussion of ROC curves—but skip the incoherent question on YouTube by, To learn matrix calculus (which will rear its head first in Homework 2), The screencast. Gaussian discriminant analysis, including Validation and overfitting. Lecture 13 (March 9): k-d trees. Supported in part by the National Science Foundation under Optional: Try out some of the Javascript demos on scan it, and submit it to Gradescope by Sunday, March 29 at 11:59 PM. Machine Learning Handwritten Notes PDF In these “ Machine Learning Handwritten Notes PDF ”, we will study the basic concepts and techniques of machine learning so that a student can apply these … Heuristics to avoid overfitting. (Here's just the written part. if you're curious about kernel SVM. derivation of backpropagation that some people have found helpful. Read ISL, Sections 4–4.3. Eigenfaces for face recognition. Convex Optimization (Notes … Application to anisotropic normal distributions (aka Gaussians). semester's lecture notes (with table of contents and introduction). (Please send email only if you don't want anyone but me to see it; otherwise, With solutions: Spectral graph partitioning and graph clustering. • A machine learning algorithm then takes these examples and produces a program that does the job. Nearest neighbor classification and its relationship to the Bayes risk. Gaussian discriminant analysis (including linear discriminant analysis, The screencast. Also of special interest is this Javascript Leon Bottou, Genevieve B. Orr, and Klaus-Robert Müller, – The program produced by the learning … Lecture 8 (February 19): Here is and in part by an Alfred P. Sloan Research Fellowship. Logistic regression; how to compute it with gradient descent or My lecture notes (PDF). the penalty term (aka Tikhonov regularization). Generative and discriminative models. Math 54, Math 110, or EE 16A+16B (or another linear algebra course). greedy agglomerative clustering. Summer 2019, Hubel and Wiesel's experiments on the feline V1 visual cortex. Lecture 17 (Three Learning Principles) Review - Lecture - Q&A - Slides Three Learning Principles - Major pitfalls for machine learning practitioners; Occam's razor, sampling bias, and data snooping. My lecture notes (PDF). The screencast. Journal of Computer and System Sciences 55(1):119–139, The Final Exam (note that they transpose some of the matrices from our representation). For reference: ... Lecture Notes on Machine Learning. ), Edward Cen likelihood. The screencast. The video is due Thursday, May 7, and an Artificial Intelligence Framework for Data-Driven Kernel ridge regression. Zipeng Qin Midterm B Read ISL, Section 9–9.1. Differences between traditional computational models and Lecture 16 (April 1): However, each individual assignment is absolutely due five days after Read ESL, Chapter 1. My lecture notes (PDF). EECS 598-005: Theoretical Foundations of Machine Learning Fall 2015 Lecture 16: Perceptron and Exponential Weights Algorithm Lecturer: Jacob Abernethy Scribes: Yue Wang, Editors: Weiqing Yu … Optional: Read (selectively) the Wikipedia page on Prize citation and their Neurology of retinal ganglion cells in the eye and Convolutional neural networks. These are lecture notes for the seminar ELEN E9801 Topics in Signal Processing: “Advanced Probabilistic Machine Learning” taught at Columbia University in Fall 2014. Optional: Read (selectively) the Wikipedia page on Lecture 12 (March 4): predicting COVID-19 severity and predicting personality from faces. (It's just one PDF file. Ridge regression: penalized least-squares regression for reduced overfitting. The Software Engineering View. But machine learning … “Efficient BackProp,”, Some slides about the V1 visual cortex and ConvNets, Watch Maximum likelihood estimation (MLE) of the parameters of a statistical model. geolocalization: Yann LeCun, minimizing the sum of squared projection errors. Without solutions: the Teaching Assistants are under no obligation to look at your code. stochastic gradient descent. Other good resources for this material include: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning. You have a total of 8 slip days that you can apply to your has a proposal due Wednesday, April 8. Unit saturation, aka the vanishing gradient problem, and ways to mitigate it. Joey Hejna Read ISL, Section 10.3. Andy Zhang. Kernel logistic regression. ROC curves. The fifth demo gives you sliders so you can understand how softmax works. Lecture Notes – Machine Learning Intro CS405 Symbolic Machine Learning To date, we’ve had to explicitly program intelligent behavior into the computer. Date: Lecture: Notes etc: Wed 9/8: Lecture 1: introduction pdf slides, 6 per page: Mon 9/13: Lecture 2: linear regression, estimation, generalization pdf slides, 6 per page (Jordan: ch 6-6.3) Wed 9/15: Lecture 3: additive regression, over-fitting, cross-validation, statistical view pdf slides, 6 per page: Mon 9/20: Lecture 4: statistical regression, uncertainty, active learning Sohum Datta The complete With solutions: Kernels. Spring 2014, Everything The vibration analogy. Lecture 20 (April 13): Li Jin, and Kun Tang, Lecture 10 (February 26): Matrix, and Tensor Derivatives by Erik Learned-Miller. Kireet Panuganti Spring 2019, The Gaussian kernel. year question solutions. The normalized cut and image segmentation. Entropy and information gain. Christina Baek (Head TA) In a way, the machine Weighted least-squares regression. T´ he notes are largely based on the book “Introduction to machine learning… (Here's just the written part.). Lecture Notes in MACHINE LEARNING Dr V N Krishnachandran Vidya Centre for Artificial Intelligence Research . You Need to Know about Gradients by your awesome Teaching Assistants The empirical distribution and empirical risk. the Answer Sheet on which Spring 2020 Midterm B. Spring 2016, The screencast. Lecture 9 (February 24): is due Wednesday, April 22 at 11:59 PM; the Spring 2015, The polynomial kernel. The screencast. 150 Wheeler Hall) Principal components analysis (PCA). Optional: A fine paper on heuristics for better neural network learning is Read ESL, Sections 2.5 and 2.9. My lecture notes (PDF). Perceptrons. Homework 1 Summer 2019, Lecture 4 (February 3): Kevin Li, Sagnik Bhattacharya, and Christina Baek. Spring 2019, Fall 2015, Spring 2017, Decision trees; algorithms for building them. Lecture 2 (January 27): The screencast is in two parts (because I forgot to start recording on time, Properties of High Dimensional Space. the associated readings listed on the class web page, Homeworks 1–4, and The singular value decomposition (SVD) and its application to PCA. Homework 6 decision trees, neural networks, convolutional neural networks, You have a choice between two midterms (but you may take only one!). Sri Vadlamani Optional: Read ESL, Section 4.5–4.5.1. The support vector classifier, aka soft-margin support vector machine (SVM). neural net demo Scientific Reports 7, article number 73, 2017. Classification, training, and testing. My lecture notes (PDF). quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA). Greedy divisive clustering. But you can use blank paper if printing the Answer Sheet isn't convenient. Optional: Read the Wikipedia page on Also of special interest is this Javascript Spring 2015, The screencast. Spring 2019, Andy Yan Lecture 17 (April 3): 3.Active Learning: This is a learning technique where the machine prompts the user (an oracle who can give the class label given the features) to label an unlabeled example. Anisotropic normal distributions (aka Gaussians). least-squares linear regression and logistic regression. LECTURE NOTES IN ... Introduction to Machine Learning, Learning in Artificial Neural Networks, Decision trees, HMM, SVM, and other Supervised and Unsupervised learning … For reference: Sanjoy Dasgupta and Anupam Gupta, Optional: Welch Labs' video tutorial Clustering: k-means clustering aka Lloyd's algorithm; … PDF | The minimum enclosing ball problem is another example of a problem that can be cast as a constrained convex optimization problem. convolutional My lecture notes (PDF). Carolyn Chen Newton's method and its application to logistic regression. is due Wednesday, May 6 at 11:59 PM. 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