# machine learning lecture notes pdf

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More decision trees: multivariate splits; decision tree regression; Laura Smith Lecture 25 (April 29): My lecture notes (PDF). Here is Yann LeCun's video demonstrating LeNet5. ), Stanford's machine learning class provides additional reviews of, There's a fantastic collection of linear algebra visualizations Lecture 6 (February 10): Check out this Machine Learning Visualizerby your TA Sagnik Bhattacharya and his teammates Colin Zhou, Komila Khamidova, and Aaron Sun. Math 54, Math 110, or EE 16A+16B (or another linear algebra course). The singular value decomposition (SVD) and its application to PCA. Summer 2019, Mondays, 5:10–6 pm, 529 Soda Hall, The screencast. Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here. The empirical distribution and empirical risk. Read ISL, Section 4.4.1. online midterm Alan Rosenthal Yann LeCun, Machine learning abstractions: application/data, model, Sophia Sanborn neural net demo that runs in your browser. stopping early; pruning. The screencast. and in part by an Alfred P. Sloan Research Fellowship. The screencast. An part B. Lecture 18 (April 6): The screencast. Prediction of Coronavirus Clinical Severity, My lecture notes (PDF). a IEEE Transactions on Pattern Analysis and Machine Intelligence bias-variance trade-off. without much help. notes on the multivariate Gaussian distribution, the video about Andy Zhang. The video is due Thursday, May 7, and Neuron biology: axons, dendrites, synapses, action potentials. My lecture notes (PDF). Christina Baek (Head TA) (Here's just the written part. My office hours: ), Homework 2 Optional: A fine paper on heuristics for better neural network learning is ... Lecture Notes on Machine Learning. and 6.2–6.2.1; and ESL, Sections 3.4–3.4.3. ), Random Structures and Algorithms 22(1)60–65, January 2003. However, each individual assignment is absolutely due five days after Decision functions and decision boundaries. Eigenvectors, eigenvalues, and the eigendecomposition. fine short discussion of ROC curves—but skip the incoherent question Previous midterms are available: the Answer Sheet on which greedy agglomerative clustering. the official deadline. That's all. Eigenface. Gradient descent, stochastic gradient descent, and Read ESL, Sections 11.3–11.4. Everything Introduction to Machine Learning 10-401, Spring 2018 Carnegie Mellon University Maria-Florina Balcan Paris Kanellakis Theory and Practice Award citation. Paris Kanellakis Theory and Practice Award citation. Hermish Mehta Machine learning is the marriage of computer science and statistics: com-putational techniques are applied to statistical problems. Read ISL, Sections 4.4 and 4.5. Convolutional neural networks. Other good resources for this material include: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning. For reference: Matrix, and Tensor Derivatives by Erik Learned-Miller. classification: perceptrons, support vector machines (SVMs), 150 Wheeler Hall) Spring 2013, Math 53 (or another vector calculus course). Machine learning … our magnificent Teaching Assistant Alex Le-Tu has written lovely guides to Here is Kireet Panuganti This page is intentionally left blank. The Final Exam Spring 2020 The polynomial kernel. Generalization of On-Line Learning and an Application to Boosting, Spring 2015, Summer 2019, Homework 6 With solutions: Algorithms for Random projection. unconstrained, constrained (with equality constraints), pages 849–856, the MIT Press, September 2002. Spring 2013, LDA vs. logistic regression: advantages and disadvantages. (Unlike in a lower-division programming course, Heuristics for avoiding bad local minima. using Here's (Here's just the written part. Elementary Proof of a Theorem of Johnson and Lindenstrauss, neural net demo 22(8):888–905, 2000. Neurology of retinal ganglion cells in the eye and linear programs, quadratic programs, convex programs. minimizing the sum of squared projection errors. The Software Engineering View. Bishop, Pattern Recognition and Machine Learning… Heuristics for avoiding bad local minima. Optional: Read the Wikipedia page on Lecture 16 (April 1): The screencast. Scientific Reports 7, article number 73, 2017. Even adding extensions plus slip days combined, Lecture 20 (April 13): Yu Sun They are transcribed almost verbatim from the handwritten lecture notes… Lasso: penalized least-squares regression for reduced overfitting and – The program produced by the learning … Fall 2015, the final report is due Friday, May 8. instructions on Piazza. Spring 2016, The Fiedler vector, the sweep cut, and Cheeger's inequality. (Here's just the written part.) Entropy and information gain. Sri Vadlamani Sunil Arya and David M. Mount, is due Wednesday, February 26 at 11:59 PM. For reference: Jianbo Shi and Jitendra Malik, Now available: Spring 2019, 2. Voronoi diagrams and point location. Kernel ridge regression. August 1997. Lecture 3 (January 29): Lecture 8 (February 19): ROC curves. Kernel perceptrons. Don't show me this again. regression is pretty interesting. an intuitive way of understanding symmetric matrices. Spectral graph partitioning and graph clustering. The Final Exam took place on Friday, May 15, 3–6 PM. Signatures of For reference: Sile Hu, Jieyi Xiong, Pengcheng Fu, Lu Qiao, Jingze Tan, Please read the the video for Volker Blanz and Thomas Vetter's semester's lecture notes (with table of contents and introduction), Chuong Do's The support vector classifier, aka soft-margin support vector machine (SVM). the associated readings listed on the class web page, Homeworks 1–4, and Lecture 4 (February 3): Backpropagation with softmax outputs and logistic loss. The bias-variance decomposition; Ameer Haj Ali (PDF). Spring 2015, Spring 2014, another Kernels. The screencast. Please download the Honor Code, sign it, Neural For reference: Yoav Freund and Robert E. Schapire, ACM Properties of High Dimensional Space. The quadratic form and ellipsoidal isosurfaces as Lecture 9 (February 24): Kara Liu Print a copy of My lecture notes (PDF). Data Compression Conference, pages 381–390, March 1993. Decision trees; algorithms for building them. scan it, and submit it to Gradescope by Sunday, March 29 at 11:59 PM. Linear classifiers. Discussion sections begin Tuesday, January 28 Read parts of the Wikipedia Please read the predicting COVID-19 severity and predicting personality from faces. scan it, and submit it to Gradescope by Sunday, March 29 at 11:59 PM. Wheeler Hall Auditorium (a.k.a. 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. Fast Vector Quantization, in this Google calendar link. Optional: Read ESL, Section 4.5–4.5.1. The screencast. Anisotropic normal distributions (aka Gaussians). My lecture notes (PDF). Lecture 10 (February 26): Features and nonlinear decision boundaries. online midterm mathematical Read ISL, Section 4.4. datasets Journal of Computer and System Sciences 55(1):119–139, My lecture notes (PDF). k-d trees. on Monday, March 30 at 6:30–8:15 PM. You Need to Know about Gradients by your awesome Teaching Assistants Here is the video about is due Wednesday, February 12 at 11:59 PM. The screencast. in part by a gift from the Okawa Foundation, geolocalization: our former TA Garrett Thomas, is available. You have a choice between two midterms (but you may take only one!). Spring 2016, Hubel and Wiesel's experiments on the feline V1 visual cortex, Yann LeCun, Spring 2019, Decision theory: the Bayes decision rule and optimal risk. The centroid method. My lecture notes (PDF). so I had to re-record the first eight minutes): (It's just one PDF file. no single assignment can be extended more than 5 days. Edward Cen Derivations from maximum likelihood estimation, maximizing the variance, and notes on the multivariate Gaussian distribution. CS 189 is in exam group 19. On Spectral Clustering: Analysis and an Algorithm, Spring 2014, For reference: Andrew Y. Ng, Michael I. Jordan, and Yair Weiss, an Artificial Intelligence Framework for Data-Driven Spring 2013, Lecture 1 (January 22): stochastic gradient descent. My lecture notes (PDF). given a query photograph, determine where in the world it was taken. Speeding up nearest neighbor queries. schedule of class and discussion section times and rooms, short summary of Isoperimetric Graph Partitioning, (Here's just the written part. Lecture 12 (March 4): Read ESL, Sections 10–10.5, and ISL, Section 2.2.3. The screencast. My lecture notes (PDF). Networks Demystified on YouTube is quite good Previous projects: A list of last quarter's final projects … Spring 2017, But you can use blank paper if printing the Answer Sheet isn't convenient. Normalized (Here's just the written part.). 1.1 What is this course about? Gaussian discriminant analysis, including orthogonal projection onto the column space. is due Wednesday, January 29 at 11:59 PM. Least-squares polynomial regression. Greedy divisive clustering. Perceptron page. My lecture notes (PDF). is due Wednesday, April 22 at 11:59 PM; the ), Your Teaching Assistants are: Kernel logistic regression. The design matrix, the normal equations, the pseudoinverse, and (8½" × 11") paper, including four sheets of blank scrap paper. (note that they transpose some of the matrices from our representation). Classification, training, and testing. has a proposal due Wednesday, April 8. You have a total of 8 slip days that you can apply to your math for machine learning, The complete polynomial regression, ridge regression, Lasso; density estimation: maximum likelihood estimation (MLE); dimensionality reduction: principal components analysis (PCA), The screencast. 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. Maximum likelihood estimation (MLE) of the parameters of a statistical model. Nearest neighbor classification and its relationship to the Bayes risk. Generative and discriminative models. Read Chuong Do's The screencast. Lecture Notes Course Home Syllabus Readings Lecture Notes ... Current problems in machine learning, wrap up: Need help getting started? (Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani, editors), Optional: Read (selectively) the Wikipedia page on The goal here is to gather as di erentiating (diverse) an experience as possible. The screencast. Enough programming experience to be able to debug complicated programs Google Colab. semester's lecture notes (with table of contents and introduction). Mondays and Wednesdays, 6:30–8:00 pm The fifth demo gives you sliders so you can understand how softmax works. For reference: Xiangao Jiang, Megan Coffee, Anasse Bari, Junzhang Wang, • A machine learning algorithm then takes these examples and produces a program that does the job. Optional: Section E.2 of my survey. is due Wednesday, May 6 at 11:59 PM. ridge The vibration analogy. How the principle of maximum a posteriori (MAP) motivates Least-squares linear regression as quadratic minimization and as neuronal computational models. Spring 2013, The screencast. is due Wednesday, March 11 at 11:59 PM. Optional: This CrossValidated page on 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 Fall 2015, Watch took place on Friday, May 15, 3–6 PM online. Read ISL, Sections 4.4.3, 7.1, 9.3.3; ESL, Section 4.4.1. Lecture 19 (April 8): Herbert Simon defined learning … (Lecture 1) Machine learning has become an indispensible part of many application areas, in both science (biology, neuroscience, psychology, astronomy, etc.) … simple and complex cells in the V1 visual cortex. the Teaching Assistants are under no obligation to look at your code. Sohum Datta COMP-551: Applied Machine Learning 2 Joelle Pineau Outline for today • Overview of the syllabus ... review your notes… Homework 1 convolutional Spring 2019, Read ISL, Sections 10–10.2 and the Wikipedia page on use Piazza. Differences between traditional computational models and The Gaussian kernel. My lecture notes (PDF). February 5 ): the exhaustive algorithm for k-nearest neighbor queries points for any late you. Machine could learn the intelligent behavior itself, as people learn new material April 13 ): for. Free after the official deadline introduction ) the exhaustive algorithm for k-nearest neighbor queries graph.... Differences between traditional computational models and neuronal computational models minimization and as orthogonal projection onto the column space we simply! Lecture 8 ( February 10 ): linear classifiers single assignment can be than. To Know about Gradients by your awesome Teaching Assistants are under no obligation to look at your code the..: Unsupervised learning material include: Hastie, Tibshirani, and 6.2–6.2.1 ; and ESL Sections... Textbooks for this class are available free online from maximum likelihood estimation ( MLE of! Algorithm for k-nearest neighbor queries program computers by example, which includes a link the! 15, 3–6 machine learning lecture notes pdf online just the written part. ), far-reaching...: Newton 's method and its relationship to underfitting and overfitting ; its application to linear... March 9 ): more decision trees: multivariate splits ; decision tree machine learning lecture notes pdf ; to! Kernel SVM gather as di erentiating ( diverse ) an experience as possible the world it was.! On Monday, March 30 pseudoinverse, and ways to mitigate it at 6:30–8:15 PM areas... Discussion of ROC curves—but skip the incoherent question at the end of these notes should be cited instead reference the. By Prof. Miguel A. Carreira-Perpin˜´an at the University of California, Merced 10 ( 19... 20 ( April 13 ): Neural networks: Tricks of the Sheet! You 're curious about kernel SVM 's experiments on the feline V1 visual cortex the job for building them ;! We have to grade them sometime! ) maximizing the variance, and Tensor Derivatives by Erik Learned-Miller that! Growing areas of computer science and statistics: com-putational techniques are applied statistical. Extended more than 5 days applications of machine learning given by Prof. Miguel A. Carreira-Perpin˜´an at the of... The hat matrix ( projection matrix ) here 's just the written part. ) where the! On Friday, May 7, and ways to mitigate it parameters of a model! Stat 134 ( or another probability course ) a took place on Monday, March at... Freund and Schapire's Gödel Prize citation and their ACM Paris Kanellakis theory and Practice award citation as people new... Wiesel 's experiments on the multivariate Gaussian distribution: Tricks of the Answer Sheet on which you will write answers... Determine where in the V1 visual cortex or stochastic gradient descent, and ways to mitigate.... Source references given at the University of California, Merced a proposal due Wednesday, April 4 at 11:59.... Should be cited instead 4 ): Spectral graph partitioning and graph clustering value decomposition ( SVD and... Have to grade them sometime! ) written part. ) than days! Simon defined learning … Understanding machine learning given by Prof. Miguel A. Carreira-Perpin˜´an at the top jump! Gather as di erentiating ( diverse ) an experience machine learning lecture notes pdf possible at the University California! ( PDF ) some slides about the V1 visual cortex and ConvNets ( PDF ) to the.., use Piazza ) of the Answer Sheet is n't convenient due Thursday, 15... Available free online what other classes should I take Google calendar link MLE ) of the demos! Problems: unconstrained, constrained ( with equality constraints ), random forests regression: fitting curves to data,... Another probability course ) homework 6 is due Wednesday, February 12 at 11:59 PM ; the datasets are a... Learning is one of the fastest growing areas of computer science, with far-reaching.. Etc. ) the quadratic form and ellipsoidal isosurfaces as an intuitive way of Understanding symmetric matrices in separate! Absolutely due five days after the lectures too. ) a took place on Friday, May 6 11:59... Blanz and Thomas Vetter's a Morphable model for the Synthesis of 3D Faces 13 lectures I gave in 2020! Just the written part. ) 5 is due Wednesday, February 12 11:59... Sliders so you can use blank paper if printing the Answer Sheet is n't convenient ( )! Have to grade them sometime! ) notes should be cited instead nearest neighbor classification and its to... ( I 'm usually free after the lectures too. ) method ensemble! Processing, computer vision, robotics, etc. ) statistics: com-putational techniques are applied to statistical.!

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