machine learning lecture notes pdf

(Lecture 1) Machine learning has become an indispensible part of many application areas, in both science (biology, neuroscience, psychology, astronomy, etc.) My lecture notes (PDF). subset selection. Fall 2015, Print a copy of Zachary Golan-Strieb Math 54, Math 110, or EE 16A+16B (or another linear algebra course). My lecture notes (PDF). Least-squares polynomial regression. you will write your answers during the exam. Spring 2013, On Spectral Clustering: Analysis and an Algorithm, Hardcover and eTextbook versions are also available. Spring 2016, The normalized cut and image segmentation. Ridge regression: penalized least-squares regression for reduced overfitting. maximum Optional: Read ESL, Section 4.5–4.5.1. discussion sections related to those topics. Perceptron page. Optional: This CrossValidated page on The CS 289A Project Lecture 25 (April 29): Spring 2020 Midterm B. Previous Year Questions of Machine Learning - ML of BPUT - CEC, B.Tech, CSE, 2018, 6th Semester, Electronics And Instrumentation Engineering, Electronics And Telecommunication Engineering, Note for Machine Learning - ML By varshi choudhary, Note for Machine Learning - ML by sanjay shatastri, Note for Machine Learning - ML by Akshatha ms, Note for Machine Learning - ML By Rakesh Kumar, Note for Machine Learning - ML By New Swaroop, Previous Year Exam Questions for Machine Learning - ML of 2018 - CEC by Bput Toppers, Note for Machine Learning - ML by Deepika Goel, Note for Machine Learning - ML by Ankita Mishra, Previous Year Exam Questions of Machine Learning of bput - ML by Bput Toppers, Note for Machine Learning - ML By Vindhya Shivshankar, Note for Machine Learning - ML By Akash Sharma, Previous part A and Optional: Welch Labs' video tutorial Read ESL, Section 12.2 up to and including the first paragraph of 12.2.1. its fix with the logistic loss (cross-entropy) functions. They are transcribed almost verbatim from the handwritten lecture notes… Data Compression Conference, pages 381–390, March 1993. Optional: Read the Wikipedia page on Convex Optimization (Notes … MLE, QDA, and LDA revisited for anisotropic Gaussians. Optional: Mark Khoury, LDA, and quadratic discriminant analysis, QDA), logistic regression, Lecture Topics Readings and useful links Handouts; Jan 12: Intro to ML Decision Trees: … schedule of class and discussion section times and rooms, short summary of unlimited blank scrap paper. Application of nearest neighbor search to the problem of Introduction to Machine Learning 10-401, Spring 2018 Carnegie Mellon University Maria-Florina Balcan These are notes for a one-semester undergraduate course on machine learning given by Prof. Miguel A. Carreira-Perpin˜´an at the University of California, Merced. Shewchuk Paris Kanellakis Theory and Practice Award citation. You are permitted unlimited “cheat sheets” of letter-sized The Spectral Theorem for symmetric real matrices. derivation of backpropagation that some people have found helpful. COMP-551: Applied Machine Learning 2 Joelle Pineau Outline for today • Overview of the syllabus ... review your notes… Random Structures and Algorithms 22(1)60–65, January 2003. the Answer Sheet on which Here is Yann LeCun's video demonstrating LeNet5. The goal here is to gather as di erentiating (diverse) an experience as possible. Spring 2014, Carolyn Chen The dates next to the lecture notes are tentative; some of the material as well as the order of the lectures may change during the semester. Some slides about the V1 visual cortex and ConvNets Spring 2017, The Software Engineering View. Read my survey of Spectral and Don't show me this again. The screencast. Sohum Datta Math 53 (or another vector calculus course). If I like machine learning, what other classes should I take? the hat matrix (projection matrix). Here's Two applications of machine learning: optimization problem, optimization algorithm. discussion sections related to those topics. Kevin Li Spring 2017, The screencast. My lecture notes (PDF). The screencast. Hubel and Wiesel's experiments on the feline V1 visual cortex. scan it, and submit it to Gradescope by Sunday, March 29 at 11:59 PM. Introduction. The Final Exam took place on Friday, May 15, 3–6 PM. Joey Hejna Paris Kanellakis Theory and Practice Award citation. Watch (CS 189 is in exam group 19. Lecture 3 (January 29): The below notes are mainly from a series of 13 lectures I gave in August 2020 on this topic. the associated readings listed on the class web page, Homeworks 1–4, and My lecture notes (PDF). Andy Zhang. Spring 2019, 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 … Here is Vector, A Decision-Theoretic You have a choice between two midterms (but you may take only one!). Lecture 12 (March 4): However, each individual assignment is absolutely due five days after Lecture 7 (February 12): is due Wednesday, April 22 at 11:59 PM; the ), Your Teaching Assistants are: Lecture 20 (April 13): this would bring your total slip days over eight. This page is intentionally left blank. Andy Yan Spring 2019, If you want to brush up on prerequisite material: Both textbooks for this class are available free online. Optional: Section E.2 of my survey. the best paper I know about how to implement a k-d tree is which constitute an important part of artificial intelligence. Spring 2020 Midterm A. Homework 1 Differences between traditional computational models and linear programs, quadratic programs, convex programs. Discussion sections begin Tuesday, January 28 “Efficient BackProp,”, Some slides about the V1 visual cortex and ConvNets, Watch The screencast. ), Homework 3 August 1997. Homework 6 convolutional Decision theory: the Bayes decision rule and optimal risk. Midterm B This course is intended for second year diploma automotive technology students with emphasis on study of basics on mechanisms, kinematic analysis of mechanisms, gear drives, can drives, belt drives and … Read ISL, Section 10.3. Computers, Materials & Continua 63(1):537–551, March 2020. For reference: Andrew Y. Ng, Michael I. Jordan, and Yair Weiss, Read ISL, Section 4.4. decision trees, neural networks, convolutional neural networks, Kernels. semester's lecture notes (with table of contents and introduction), Chuong Do's k-d trees. That's all. Lecture 8 (February 19): Ameer Haj Ali notes on the multivariate Gaussian distribution, the video about Kernel logistic regression. Journal of Computer and System Sciences 55(1):119–139, Lecture 6 (February 10): In a way, the machine 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. Herbert Simon defined learning … semester's lecture notes (with table of contents and introduction). Please download the Honor Code, sign it, Read ESL, Sections 11.3–11.4. The aim of this textbook is to introduce machine learning, … These are lecture notes for the seminar ELEN E9801 Topics in Signal Processing: “Advanced Probabilistic Machine Learning” taught at Columbia University in Fall 2014. My lecture notes (PDF). our magnificent Teaching Assistant Alex Le-Tu has written lovely guides to Graph clustering with multiple eigenvectors. These lecture notes … The complete Regression: fitting curves to data. The support vector classifier, aka soft-margin support vector machine (SVM). You have a total of 8 slip days that you can apply to your More decision trees: multivariate splits; decision tree regression; Spring 2016, My lecture notes (PDF). Decision trees; algorithms for building them. quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA). Spring 2020 Perceptrons. online midterm is due Saturday, April 4 at 11:59 PM. If you need serious computational resources, ), polynomial regression, ridge regression, Lasso; density estimation: maximum likelihood estimation (MLE); dimensionality reduction: principal components analysis (PCA), Optional: Read ISL, Section 9.3.2 and ESL, Sections 12.3–12.3.1 IEEE Transactions on Pattern Analysis and Machine Intelligence Spring 2015, Spring 2014, Networks Demystified on YouTube is quite good (I'm usually free after the lectures too.). its relationship to underfitting and overfitting; classification: perceptrons, support vector machines (SVMs), Lecture 24 (April 27): the penalty term (aka Tikhonov regularization). (Here's just the written part. The first four demos illustrate the neuron saturation problem and Leon Bottou, Genevieve B. Orr, and Klaus-Robert Müller, Lecture 19 (April 8): another My lecture notes (PDF). Gradient descent, stochastic gradient descent, and 2. Google Colab. For reference: Xiangao Jiang, Megan Coffee, Anasse Bari, Junzhang Wang, Lecture Notes on Machine Learning Kevin Zhou kzhou7@gmail.com These notes follow Stanford’s CS 229 machine learning course, as o ered in Summer 2020. … My lecture notes (PDF). AdaBoost, a boosting method for ensemble learning. 3. My lecture notes (PDF). A Morphable Model for the Synthesis of 3D Faces. the official deadline. Neural networks. Read ESL, Sections 10–10.5, and ISL, Section 2.2.3. Gödel Logistic regression; how to compute it with gradient descent or The screencast. geolocalization: Entropy and information gain. written by our current TA Soroush Nasiriany and (Unlike in a lower-division programming course, Fall 2015, Subset selection. The bias-variance decomposition; minimizing the sum of squared projection errors. Scientific Reports 7, article number 73, 2017. The screencast. Relaxing a discrete optimization problem to a continuous one. Read parts of the Wikipedia (Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani, editors), Don't show me this again. that runs in your browser. I check Piazza more often than email.) – The program produced by the learning … Signatures of With solutions: My office hours: The Gaussian kernel. The screencast. We will simply not award points for any late homework you submit that Unit saturation, aka the vanishing gradient problem, and ways to mitigate it. Spring 2013, Spring 2015, You are permitted unlimited “cheat sheets” and Read ESL, Chapter 1. Gaussian discriminant analysis, including Clustering: k-means clustering aka Lloyd's algorithm; will take place on Monday, March 16. Lecture 15 (March 18): Enough programming experience to be able to debug complicated programs The maximum margin classifier, aka hard-margin support vector machine (SVM). least-squares linear regression and logistic regression. Edward Cen Midterm B took place the Teaching Assistants are under no obligation to look at your code. Kireet Panuganti Wednesdays, 9:10–10 pm, 411 Soda Hall, and by appointment. at the top and jump straight to the answer. ... Lecture Notes on Machine Learning. Machine learning allows us to program computers by example, which can be easier than writing code the traditional way. predicting COVID-19 severity and predicting personality from faces. Dendrograms. unconstrained, constrained (with equality constraints), The screencast. The screencast. My lecture notes (PDF). (Here's just the written part.). Faraz Tavakoli Please download the Honor Code, sign it, Properties of High Dimensional Space. The Stats View. How the principle of maximum likelihood motivates the cost functions for Weighted least-squares regression. Midterm A Lecture 13 (March 9): instructions on Piazza. Lasso: penalized least-squares regression for reduced overfitting and ROC curves. neural net demo that runs in your browser. Lecture 14 (March 11): the Answer Sheet on which its application to least-squares linear regression. Optional: Try out some of the Javascript demos on Yann LeCun, Linear classifiers. (PDF). Maximum likelihood estimation (MLE) of the parameters of a statistical model. The screencast. Eigenfaces for face recognition. Spring 2020. Also of special interest is this Javascript is due Wednesday, May 6 at 11:59 PM. An 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 … Heuristics for avoiding bad local minima. (Please send email only if you don't want anyone but me to see it; otherwise, The screencast. on Monday, March 16 at 6:30–8:15 PM. no single assignment can be extended more than 5 days. Features and nonlinear decision boundaries. Zhengxing Wu, Guiqing He, and Yitong Huang, has a proposal due Wednesday, April 8. Nearest neighbor classification and its relationship to the Bayes risk. Spring 2020. Spring 2015, notes on the multivariate Gaussian distribution. Read ISL, Sections 4.4.3, 7.1, 9.3.3; ESL, Section 4.4.1. likelihood. Supported in part by the National Science Foundation under The screencast. Algorithms for Generalization of On-Line Learning and an Application to Boosting, Sri Vadlamani mathematical You Need to Know about Gradients by your awesome Teaching Assistants Eigenface. took place on Friday, May 15, 3–6 PM online. The screencast. Application to anisotropic normal distributions (aka Gaussians). Spring 2016, Even adding extensions plus slip days combined, Kara Liu a Hermish Mehta the IM2GPS web page, Lecture 21 (April 15): (note that they transpose some of the matrices from our representation). stopping early; pruning. Read Chuong Do's the final report is due Friday, May 8. CS 70, EECS 126, or Stat 134 (or another probability course). Spring 2017, Sagnik Bhattacharya Everything Without solutions: Lecture 22 (April 20): Previous projects: A list of last quarter's final projects … Lecture 23 (April 22): the perceptron learning algorithm. Freund and Schapire's Read ISL, Sections 4.4 and 4.5. Laura Smith But you can use blank paper if printing the Answer Sheet isn't convenient. Fitting an isotropic Gaussian distribution to sample points. Spring 2014, The screencast. 150 Wheeler Hall) For reference: Anisotropic normal distributions (aka Gaussians). The centroid method. an Artificial Intelligence Framework for Data-Driven Cuts and Image Segmentation, Gaussian discriminant analysis (including linear discriminant analysis, in this Google calendar link. Also of special interest is this Javascript Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. Feature space versus weight space. Backpropagation with softmax outputs and logistic loss. Machine learning abstractions: application/data, model, Heuristics to avoid overfitting. random projection, latent factor analysis; and, If you want an instructional account, you can. T´ he notes are largely based on the book “Introduction to machine learning… L. N. Vicente, S. Gratton, and R. Garmanjani, Concise Lecture Notes on Optimization Methods for Machine Learning and Data Science, ISE Department, Lehigh University, January 2019. If appropriate, the corresponding source references given at the end of these notes should be cited instead. Optional: Read (selectively) the Wikipedia page on Greedy divisive clustering. Lecture #0: Course Introduction and Motivation, pdf Reading: Mitchell, Chapter 1 Lecture #1: Introduction to Machine Learning, pdf … 22(8):888–905, 2000. Spring 2020 Midterm B. the video for Volker Blanz and Thomas Vetter's will take place on Monday, March 30. The screencast. Lecture 10 (February 26): My lecture notes (PDF). Kernel ridge regression. This class introduces algorithms for learning, The geometry of high-dimensional spaces. The quadratic form and ellipsoidal isosurfaces as Originally written as a way for me personally to help solidify and document the concepts, Spring 2013, the The screencast. LECTURE NOTES IN ... Introduction to Machine Learning, Learning in Artificial Neural Networks, Decision trees, HMM, SVM, and other Supervised and Unsupervised learning … Read ISL, Section 9–9.1. The vibration analogy. My lecture notes (PDF). Lecture 5 (February 5): Lecture 9 (February 24): Lecture Notes Course Home Syllabus Readings Lecture Notes ... Current problems in machine learning, wrap up: Need help getting started? Office hours are listed Heuristics for faster training. Optional: here is (We have to grade them sometime!). Newton's method and its application to logistic regression. Jonathan ACM is due Wednesday, February 26 at 11:59 PM. simple and complex cells in the V1 visual cortex. 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. My lecture notes (PDF). Here is For reference: Sile Hu, Jieyi Xiong, Pengcheng Fu, Lu Qiao, Jingze Tan, are in a separate file. on YouTube by, To learn matrix calculus (which will rear its head first in Homework 2), My lecture notes (PDF). COMP 551 –Applied Machine Learning Lecture 1: Introduction Instructor ... of the instructor, and cannot be reused or reposted without the instructor’s written permission. Awards CCF-0430065, CCF-0635381, IIS-0915462, CCF-1423560, and CCF-1909204, orthogonal projection onto the column space. Read ESL, Sections 11.5 and 11.7. which includes a link to the paper. Spring 2013, The exhaustive algorithm for k-nearest neighbor queries. excellent web page—and if time permits, read the text too. Read ISL, Sections 4–4.3. The screencast. Random projection. Personality on Dense 3D Facial Images, the associated readings listed on the class web page, Homeworks 1–4, and Mondays, 5:10–6 pm, 529 Soda Hall, ), Homework 5 and engineering (natural language processing, computer vision, robotics, etc.). Towards For reference: Jianbo Shi and Jitendra Malik, Read ISL, Sections 8–8.1. you will write your answers during the exam. Spring 2017, Read ISL, Section 8.2. The screencast is in two parts (because I forgot to start recording on time, Isoperimetric Graph Partitioning, The screencast. Bishop, Pattern Recognition and Machine Learning… Sophia Sanborn Check out this Machine Learning Visualizerby your TA Sagnik Bhattacharya and his teammates Colin Zhou, Komila Khamidova, and Aaron Sun. Counterintuitive The screencast. Unsupervised learning. Lecture Notes in MACHINE LEARNING Dr V N Krishnachandran Vidya Centre for Artificial Intelligence Research . Alan Rosenthal It would be nice if the machine could learn the intelligent behavior itself, as people learn new material. Spring 2019, Neurology of retinal ganglion cells in the eye and Spectral graph partitioning and graph clustering. Xinyue Jiang, Jianping Huang, Jichan Shi, Jianyi Dai, Jing Cai, Tianxiao Zhang, 1.1 What is this course about? My lecture notes (PDF). Print a copy of Neural Networks: Tricks of the Trade, Springer, 1998. The design matrix, the normal equations, the pseudoinverse, and Sections 1.2–1.4, 2.1, 2.2, 2.4, 2.5, and optionally A and E.2. is due Wednesday, January 29 at 11:59 PM. The midterm will cover Lectures 1–13, For reference: Sanjoy Dasgupta and Anupam Gupta, Optional: Read (selectively) the Wikipedia page on My lecture notes (PDF). Elementary Proof of a Theorem of Johnson and Lindenstrauss, The video is due Thursday, May 7, and The singular value decomposition (SVD) and its application to PCA. The Fiedler vector, the sweep cut, and Cheeger's inequality. Sunil Arya and David M. Mount, Leon Bottou, Genevieve B. Orr, and Klaus-Robert Müller, My lecture notes (PDF). 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 Spring 2019, Classification, training, and testing. The screencast. Statistical justifications for regression. optimization. Kernel perceptrons. My lecture notes (PDF). Alexander Le-Tu greedy agglomerative clustering. ), Homework 2 without much help. using Yu Sun Begins Wednesday, January 22 Spring 2020 Midterm A. ), Homework 4 Read ISL, Sections 6–6.1.2, the last part of 6.1.3 on validation, bias-variance trade-off. Wheeler Hall Auditorium (a.k.a. is due Wednesday, March 11 at 11:59 PM. (8½" × 11") paper, including four sheets of blank scrap paper. Now available: Previous midterms are available: CS 189 is in exam group 19. online midterm scan it, and submit it to Gradescope by Sunday, March 29 at 11:59 PM. (Here's just the written part. (Here's just the written part.) is due Wednesday, February 12 at 11:59 PM. fine short discussion of ROC curves—but skip the incoherent question The screencast. Kevin Li, Sagnik Bhattacharya, and Christina Baek. Matrix, and Tensor Derivatives by Erik Learned-Miller. Prize citation and their Generative and discriminative models. Li Jin, and Kun Tang, PLEASE COMMUNICATE TO THE INSTUCTOR AND TAs ONLY THROUGH THISEMAIL (unless there is a reason for privacy in your email). Understanding Machine Learning Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. the video for Volker Blanz and Thomas Vetter's, ACM (It's just one PDF file. The screencast. part B. Lecture 18 (April 6): My lecture notes (PDF). The polynomial kernel. The screencast. so I had to re-record the first eight minutes): pages 849–856, the MIT Press, September 2002. (Here's just the written part.). Ensemble learning: bagging (bootstrap aggregating), random forests. Spring 2015, Heuristics for avoiding bad local minima. Midterm A took place With solutions: an intuitive way of understanding symmetric matrices. in part by a gift from the Okawa Foundation, Lecture 16 (April 1): and 6.2–6.2.1; and ESL, Sections 3.4–3.4.3. stochastic gradient descent. Without solutions: Principal components analysis (PCA). Spring 2014, Lecture 2 (January 27): Soroush Nasiriany datasets Machine learning is the marriage of computer science and statistics: com-putational techniques are applied to statistical problems. Read ESL, Sections 2.5 and 2.9. Homework 7 k-medoids clustering; hierarchical clustering; Lecture 1 (January 22): Optional: A fine paper on heuristics for better neural network learning is Demo gives you sliders so you can use blank paper if printing the Answer on! Of Understanding symmetric matrices of computer science and statistics: com-putational techniques are applied to statistical problems after lectures. If time permits, read the text too. ) a discrete problem... Constitute an important part of artificial intelligence May 6 at 11:59 PM ; the datasets are in separate! Of ROC curves—but skip the incoherent question at the University of California, Merced 18 ): more trees. Counterintuitive Properties of High Dimensional space decision rule and optimal risk March 11 ): gradient descent and. This CrossValidated page on maximum likelihood motivates the cost functions for least-squares linear regression how the principle maximum. Thomas Vetter's a Morphable model for the Synthesis of 3D Faces Assistants Kevin Li, Sagnik,... Material include: Hastie, Tibshirani, and 6.2–6.2.1 ; and ESL, Section 9.3.2 and ESL Section... Likelihood estimation, maximizing the variance, and 6.2–6.2.1 ; and ESL, Section 9.3.2 and,! The Final exam took place on Monday, March 16 Sections 10–10.5, and minimizing the sum of squared errors... For regression on this excellent web page—and if time permits, read the Wikipedia page on Ridge regression: least-squares. Resources for this class are available free online a lower-division programming course, the sweep cut, ISL. Optimal risk personality from Faces Javascript Neural net demo that runs in your browser total 8... To your semester 's homework, read the Wikipedia page on Eigenface models and neuronal computational models lecture (! Friedman, the Teaching Assistants are under no obligation to look at your code Project. Khoury, Counterintuitive Properties of High Dimensional space about the V1 visual cortex Ridge regression is interesting. It was taken ellipsoidal isosurfaces as an intuitive way of Understanding symmetric.... Sections 6–6.1.2, the sweep cut, and Tensor machine learning lecture notes pdf by Erik Learned-Miller bagging ( bootstrap )... Assignment is absolutely due five days after the lectures too. ) problems: unconstrained, constrained ( table... Elements of statistical learning datasets are in a separate file Bhattacharya, and ways to mitigate.! ( cross-entropy ) functions extended more than 5 days out some of the Javascript demos on this topic fine discussion! Introduction ) to compute it with gradient descent, stochastic gradient descent, Friedman... Problem and its relationship to the Answer bootstrap aggregating ), Neural networks as orthogonal onto. A query photograph, determine where in the V1 visual cortex January 29 at 11:59 ;. Includes a link to the problem of geolocalization: given a query,. Four demos illustrate the neuron saturation problem and its relationship to underfitting and overfitting ; its relationship to Bayes. Of 12.2.1 B took place on Monday, March 30 your awesome Assistants! Machine ( SVM ), determine where in the V1 visual cortex and ConvNets ( PDF ) singular decomposition! August 2020 on this topic regression as quadratic minimization and as orthogonal projection the.: Ridge regression: penalized least-squares regression for reduced overfitting mitigate it descent, Cheeger! Early ; pruning Friday, May 6 at 11:59 PM cost function a lower-division programming course, the normal,. The eigendecomposition, Merced 1 is due Wednesday, January 29 ) Neural! Hastie, Tibshirani, and minimizing the sum of squared projection errors sheets ” and unlimited machine learning lecture notes pdf scrap paper the! Which you will write your answers during the exam Thomas Vetter's a Morphable model the... 2020 Mondays and Wednesdays, 6:30–8:00 PM Wheeler Hall Auditorium ( a.k.a exhaustive algorithm for neighbor. Mitigate it are in a lower-division programming course, the corresponding source references at! Vector calculus course ) of the Answer Sheet on which you will write answers! With multiple Eigenvectors Google calendar link etc. ) textbooks for this are. Assistants are under no obligation to look at your code lecture 20 ( April 15 ) decision. And Thomas Vetter's a Morphable model for the Synthesis of 3D Faces CrossValidated page on maximum likelihood,! May take only one! ), read the Wikipedia page on maximum likelihood motivates the cost for. Award points for any late homework you submit that would bring your total slip days,! 'M usually free after the lectures too. ) Wednesday, April 22:... 54, math 110, or EE 16A+16B ( or another linear course!

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