CS 189/289A Introduction to Machine Learning
Jonathan
Shewchuk |
This class introduces algorithms for learning, which constitute an important part of artificial intelligence.
Topics include
If you want to brush up on prerequisite material:
Both textbooks for this class are available free online. Hardcover and eTextbook versions are also available.
You have a total of 5 slip days that you can apply to your semester's homework. We will simply not award points for any late homework you submit that would bring your total slip days over five. If you are in the Disabled Students' Program and you are offered an extension, even with your extension plus slip days combined, no single assignment can be extended more than 5 days. (We have to grade them sometime!)
The CS 289A Project has a proposal due Friday, April 9. The video is due Saturday, May 8, and the final report is due Sunday, May 9. Please sign up your group for a ten-minute meeting slot with one of the TAs on this Google spreadsheet before 11:59 PM on April 4. If you need serious computational resources, our former Teaching Assistant Alex Le-Tu has written lovely guides to using Google Cloud and using Google Colab.
Homework 1 is due Wednesday, January 27 at 11:59 PM. (Here's just the written part.)
Homework 2 is due Wednesday, February 10 at 11:59 PM. Homework 2
Homework 3 is due Wednesday, February 24 at 11:59 PM. (Here's just the written part.)
Homework 4 is due Wednesday, March 10 at 11:59 PM. (Here's just the written part.)
Homework 5 is due Thursday, April 1 at 11:59 PM. (Here's just the written part.)
Homework 6 is due Wednesday, April 21 at 11:59 PM. Here's the written part and the other files.
Homework 7 is due Thursday, May 6 at 11:59 PM. Here's the written part and the other files.
The Midterm took place on Wednesday, March 17 at 7:30–9:00 PM. Please download the Honor Code, sign it, scan it, and submit it to Gradescope by Tuesday, March 16 at 11:59 PM.
Previous midterms are available: Without solutions: Spring 2013, Spring 2014, Spring 2015, Fall 2015, Spring 2016, Spring 2017, Spring 2019, Summer 2019, Spring 2020 Midterm A, Spring 2020 Midterm B, Spring 2021. With solutions: Spring 2013, Spring 2014, Spring 2015, Fall 2015, Spring 2016, Spring 2017, Spring 2019, Summer 2019, Spring 2020 Midterm A, Spring 2020 Midterm B, Spring 2021.
The Final Exam will take place on Friday, May 14, 3–6 PM. Previous final exams are available. Without solutions: Spring 2013, Spring 2014, Spring 2015, Fall 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020. With solutions: Spring 2013, Spring 2014, Spring 2015, Fall 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020.
Now available: The complete semester's lecture notes (with table of contents and introduction).
The lecture Zoom meeting numbers and passwords are available on Piazza.
Lecture 1 (January 20): Introduction. Classification, training, and testing. Validation and overfitting. Read ESL, Chapter 1. My lecture notes (PDF). The screencast.
Lecture 2 (January 25): Linear classifiers. Decision functions and decision boundaries. The centroid method. Perceptrons. Read parts of the Wikipedia Perceptron page. Optional: Read ESL, Section 4.5–4.5.1. My lecture notes (PDF). The screencast.
Lecture 3 (January 27): Gradient descent, stochastic gradient descent, and the perceptron learning algorithm. Feature space versus weight space. The maximum margin classifier, aka hard-margin support vector machine (SVM). Read ISL, Section 9–9.1. My lecture notes (PDF). The screencast.
Lecture 4 (February 1): The support vector classifier, aka soft-margin support vector machine (SVM). Features and nonlinear decision boundaries. Read ESL, Section 12.2 up to and including the first paragraph of 12.2.1. My lecture notes (PDF). The screencast.
Lecture 5 (February 3): Machine learning abstractions: application/data, model, optimization problem, optimization algorithm. Common types of optimization problems: unconstrained, constrained (with equality constraints), linear programs, quadratic programs, convex programs. Optional: Read (selectively) the Wikipedia page on mathematical optimization. My lecture notes (PDF). The screencast.
Lecture 6 (February 8): Decision theory: the Bayes decision rule and optimal risk. Generative and discriminative models. Read ISL, Section 4.4.1. My lecture notes (PDF). The screencast.
Lecture 7 (February 10): Gaussian discriminant analysis, including quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA). Maximum likelihood estimation (MLE) of the parameters of a statistical model. Fitting an isotropic Gaussian distribution to sample points. Read ISL, Section 4.4. Optional: Read (selectively) the Wikipedia page on maximum likelihood. My lecture notes (PDF). The screencast.
February 15 is Presidents' Day.
Lecture 8 (February 17): Eigenvectors, eigenvalues, and the eigendecomposition. The Spectral Theorem for symmetric real matrices. The quadratic form and ellipsoidal isosurfaces as an intuitive way of understanding symmetric matrices. Application to anisotropic normal distributions (aka Gaussians). Read Chuong Do's notes on the multivariate Gaussian distribution. My lecture notes (PDF). The screencast.
Lecture 9 (February 22): Anisotropic normal distributions (aka Gaussians). MLE, QDA, and LDA revisited for anisotropic Gaussians. Read ISL, Sections 4.4 and 4.5. My lecture notes (PDF). The screencast.
Lecture 10 (February 24): Regression: fitting curves to data. The 3-choice menu of regression function + loss function + cost function. Least-squares linear regression as quadratic minimization and as orthogonal projection onto the column space. The design matrix, the normal equations, the pseudoinverse, and the hat matrix (projection matrix). Logistic regression; how to compute it with gradient descent or stochastic gradient descent. Read ISL, Sections 4–4.3. My lecture notes (PDF). The screencast.
Lecture 11 (March 1): Newton's method and its application to logistic regression. LDA vs. logistic regression: advantages and disadvantages. ROC curves. Weighted least-squares regression. Least-squares polynomial regression. Read ISL, Sections 4.4.3, 7.1, 9.3.3; ESL, Section 4.4.1. Optional: here is a fine short discussion of ROC curves—but skip the incoherent question at the top and jump straight to the answer. My lecture notes (PDF). The screencast.
Lecture 12 (March 3): Statistical justifications for regression. The empirical distribution and empirical risk. How the principle of maximum likelihood motivates the cost functions for least-squares linear regression and logistic regression. The bias-variance decomposition; its relationship to underfitting and overfitting; its application to least-squares linear regression. Read ESL, Sections 2.5 and 2.9. Optional: Read the Wikipedia page on the bias-variance trade-off. My lecture notes (PDF). The screencast.
Lecture 13 (March 8): Ridge regression: penalized least-squares regression for reduced overfitting. How the principle of maximum a posteriori (MAP) motivates the penalty term (aka Tikhonov regularization). Subset selection. Lasso: penalized least-squares regression for reduced overfitting and subset selection. Read ISL, Sections 6–6.1.2, the last part of 6.1.3 on validation, and 6.2–6.2.1; and ESL, Sections 3.4–3.4.3. Optional: This CrossValidated page on ridge regression is pretty interesting. My lecture notes (PDF). The screencast.
Lecture 14 (March 10): Decision trees; algorithms for building them. Entropy and information gain. Read ISL, Sections 8–8.1. My lecture notes (PDF). The screencast.
Lecture 15 (March 15): More decision trees: multivariate splits; decision tree regression; stopping early; pruning. Ensemble learning: bagging (bootstrap aggregating), random forests. Read ISL, Section 8.2. My lecture notes (PDF). The screencast.
The Midterm took place on Wednesday, March 17. The midterm will cover Lectures 1–13, the associated readings listed on the class web page, Homeworks 1–4, and discussion sections related to those topics. Please download the Honor Code, sign it, scan it, and submit it to Gradescope by Tuesday, March 16 at 11:59 PM.
March 22–26 is Spring Recess.
Lecture 16 (March 29): Kernels. Kernel ridge regression. The polynomial kernel. Kernel perceptrons. Kernel logistic regression. The Gaussian kernel. Optional: Read ISL, Section 9.3.2 and ESL, Sections 12.3–12.3.1 if you're curious about kernel SVM. My lecture notes (PDF). The screencast.
Lecture 17 (March 31): Neural networks. Gradient descent and the backpropagation algorithm. Read ESL, Sections 11.3–11.4. Optional: Welch Labs' video tutorial Neural Networks Demystified on YouTube is quite good (note that they transpose some of the matrices from our representation). Also of special interest is this Javascript neural net demo that runs in your browser. Here's another derivation of backpropagation that some people have found helpful. My lecture notes (PDF). The screencast.
Lecture 18 (April 5): Neuron biology: axons, dendrites, synapses, action potentials. Differences between traditional computational models and neuronal computational models. Backpropagation with softmax outputs and logistic loss. Unit saturation, aka the vanishing gradient problem, and ways to mitigate it. Heuristics for avoiding bad local minima. Optional: Try out some of the Javascript demos on this excellent web page—and if time permits, read the text too. The first four demos illustrate the neuron saturation problem and its fix with the logistic loss (cross-entropy) functions. The fifth demo gives you sliders so you can understand how softmax works. My lecture notes (PDF). The screencast.
Lecture 19 (April 7): Heuristics for faster training. Heuristics for avoiding bad local minima. Heuristics to avoid overfitting. Convolutional neural networks. Neurology of retinal ganglion cells in the eye and simple and complex cells in the V1 visual cortex. Read ESL, Sections 11.5 and 11.7. Here is the video about Hubel and Wiesel's experiments on the feline V1 visual cortex. Here is Yann LeCun's video demonstrating LeNet5. Optional: A fine paper on heuristics for better neural network learning is Yann LeCun, Leon Bottou, Genevieve B. Orr, and Klaus-Robert Müller, “Efficient BackProp,” in G. Orr and K.-R. Müller (Eds.), Neural Networks: Tricks of the Trade, Springer, 1998. Also of special interest is this Javascript convolutional neural net demo that runs in your browser. Some slides about the V1 visual cortex and ConvNets (PDF). My lecture notes (PDF). The screencast.
Lecture 20 (April 12): Unsupervised learning. Principal components analysis (PCA). Derivations from maximum likelihood estimation, maximizing the variance, and minimizing the sum of squared projection errors. Eigenfaces for face recognition. Read ISL, Sections 10–10.2 and the Wikipedia page on Eigenface. Watch the video for Volker Blanz and Thomas Vetter's A Morphable Model for the Synthesis of 3D Faces. My lecture notes (PDF). The screencast.
Lecture 21 (April 14): The singular value decomposition (SVD) and its application to PCA. Clustering: k-means clustering aka Lloyd's algorithm; k-medoids clustering; hierarchical clustering; greedy agglomerative clustering. Dendrograms. Read ISL, Section 10.3. My lecture notes (PDF). The screencast.
Lecture 22 (April 19): Spectral graph partitioning and graph clustering. Relaxing a discrete optimization problem to a continuous one. The Fiedler vector, the sweep cut, and Cheeger's inequality. The vibration analogy. Greedy divisive clustering. The normalized cut and image segmentation. Read my survey of Spectral and Isoperimetric Graph Partitioning, Sections 1.2–1.4, 2.1, 2.2, 2.4, 2.5, and optionally A and E.2. For reference: Jianbo Shi and Jitendra Malik, Normalized Cuts and Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8):888–905, 2000. My lecture notes (PDF). The screencast.
Lecture 23 (April 21): Graph clustering with multiple eigenvectors. The geometry of high-dimensional spaces. Random projection. An application of machine learning: predicting personality from faces. Optional: Mark Khoury, Counterintuitive Properties of High Dimensional Space. Optional: Section E.2 of my survey. For reference: Andrew Y. Ng, Michael I. Jordan, and Yair Weiss, On Spectral Clustering: Analysis and an Algorithm, Advances in Neural Information Processing Systems 14 (Thomas G. Dietterich, Suzanna Becker, and Zoubin Ghahramani, editors), pages 849–856, the MIT Press, September 2002. For reference: Sanjoy Dasgupta and Anupam Gupta, An Elementary Proof of a Theorem of Johnson and Lindenstrauss, Random Structures and Algorithms 22(1)60–65, January 2003. For reference: Sile Hu, Jieyi Xiong, Pengcheng Fu, Lu Qiao, Jingze Tan, Li Jin, and Kun Tang, Signatures of Personality on Dense 3D Facial Images, Scientific Reports 7, article number 73, 2017. My lecture notes (PDF). The screencast.
Lecture 24 (April 26): AdaBoost, a boosting method for ensemble learning. Nearest neighbor classification and its relationship to the Bayes risk. Read ESL, Sections 10–10.5, and ISL, Section 2.2.3. For reference: Yoav Freund and Robert E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences 55(1):119–139, August 1997. Freund and Schapire's Gödel Prize citation and their ACM Paris Kanellakis Theory and Practice Award citation. My lecture notes (PDF). The screencast.
Lecture 25 (April 28): The exhaustive algorithm for k-nearest neighbor queries. Speeding up nearest neighbor queries. Voronoi diagrams and point location. k-d trees. Application of nearest neighbor search to the problem of geolocalization: given a query photograph, determine where in the world it was taken. If I like machine learning, what other classes should I take? For reference: the best paper I know about how to implement a k-d tree is Sunil Arya and David M. Mount, Algorithms for Fast Vector Quantization, Data Compression Conference, pages 381–390, March 1993. For reference: the IM2GPS web page, which includes a link to the paper. My lecture notes (PDF). The screencast.
The Final Exam will take place on Friday, May 14, 3–6 PM online.
Sections begin to meet on January 25.
Your Teaching Assistants are:
Kevin Li (Head TA)
Kumar Agrawal
Christina Baek
Sagnik Bhattacharya
Sohum Datta
Joey Hejna
An Ju
Zhuang Liu
Ziye Ma
Hermish Mehta
Nathan Miller
Kireet Panuganti
Deirdre Quillen
Arvind Sridhar
Arjun Sripathy
Yaodong Yu
Supported in part by the National Science Foundation under Awards CCF-0430065, CCF-0635381, IIS-0915462, CCF-1423560, and CCF-1909204, in part by a gift from the Okawa Foundation, and in part by an Alfred P. Sloan Research Fellowship.