Notes from Dr. Borkosky

due Wednesday, January 24 for remote students.

ECE6254 Course Syllabus ECE6254 Statistical Machine Learning (3-0-3) Prerequisites ECE 4270 Corequisites None Catalog Description An introduction to the theory of statistical learning and practical machine learning algorithms with applications in signal processing and data analysis. We will study both practical algorithms for statistical inference and theoretical aspects of how to reason about and work with probabilistic models. Recommended textbooks. Also, when not specified, the text links are to Moon & Stirling's. Write on one side of your paper. Office: Centergy One, Room 5212 (, The linked problem sets can be found here (the links on the 6250 site are behind a wall). The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman (2011). I will not require you to purchase any specific text, but the primary sources for the course are: Learning from Data by Abu-Mostafa, Magdon-Ismail, and Lin (2012). Accordingly, the following are guidelines that are aimed at improving the quality of homework submissions, facilitating learning, and helping the grader to quickly evaluate your submission. Throughout this course we will take a statistical perspective, which will require familiarity with basic concepts in probability (e.g., random variables, expectation, independence, joint distributions, conditional distributions, Bayes rule, and the multivariate normal distribution). There are many other books and journal papers of interest which will be listed in the resources section of the course web site.

A first model of learning: Concentration inequalities and generalization bounds, The Bayes classifier and nearest neighbors classifiers, Plugin methods I: Naïve Bayes and linear discriminant analysis, More linear classifiers: The perceptron algorithm and maximum margin hyperplanes, Theory of generalization: Dichotomies, the growth function, shattering, and break points, Theory of generalization 2: The Vapnik-Chervonenkis generalization bound, Regression, least squares, and Tikhonov regularization, The LASSO, robust regression, kernel regression, and regularization in classification, Overfitting and the bias-variance tradeoff, Dimensionality reduction, feature selection, and principal component analysis, Kernel density estimation and k-means clustering, Gaussian mixture models and expectation maximization, Spectral clustering, density-based clustering, and hierarchical clustering. This information should appear on every page of your submission. Exam information is on the Schedule page. We will consider a variety of applications, including classification, prediction, regression, clustering, modeling, and data exploration/visiualization. This course will provide an introduction to the theory of statistical learning and practical machine learning algorithms with applications in … Email: mdav (at) gatech (dot) edu (Available online as a pdf, free and legal). Spring 2015, ECE 8823, Convex Optimization: Theory, Algorithms, and Applications. Jan 07, 2020: Syllabus Overview - The Supervised Learning Problem: Jan 09, 2020: Why supervised learning may work: Jan 14, 2020: Why supervised learning may work ECE 6254 - Statistical Machine Learning.

Description. (, An Introduction to Probability Theory and its Applications, , Boyd, 2004.

We will consider a variety of applications, including classification, prediction, regression, clustering, modeling, and data exploration/visualization. We will study both practical algorithms for statistical inference and theoretical aspects of how to reason about and work with probabilistic models.

All answers must be clearly indicated (boxed if appropriate). ECE 6254: Statistical Machine Learning Spring 2017. SCHEDULE. This course will provide an introduction to the theory of statistical learning and practical machine learning algorithms with applications in signal processing and data analysis. Mark Davenport ECE 6254 Statistical Machine Learning Spring 2017 Mark A. Davenport Georgia Institute of Technology School of Electrical and Computer Engineering. ECE 6254 - Statistical Machine Learning Homework Guidelines for ECE 6254 The application of learned principles, and practice, are essential to learning new material. Resources . Textbooks . Fall 2015, ECE 6250, Advanced Topics in Digital Signal Processing.

Put your name in the upper right corner along with the date and class (ECE 6254).

Practice brevity while maintaining completeness.

If you’re unsure about taking the class, the self-assessment is here to help! ECE 6254: Statistical Machine Learning Spring 2017. Textbooks In contrast to most traditional approaches to statistical inference and signal processing, in this course we will focus on how to learn effective models from data and how to apply these models to practical signal processing problems. In contrast to most traditional approaches to statistical inference and signal processing, in this course we will focus on how to learn effective models from data and how to apply these models to practical signal processing problems. Spring 2016, ECE 6254, Statistical Learning and Signal Processing. We will cover the tools from convex optimization that we need as part of the course, but if you want to know more, this is a great resource targeted towards electrical engineers.

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