ECE Course Syllabus
ECE6254 Course Syllabus
Statistical Machine Learning (3-0-3)
- Technical Interest
- Digital Signal Processing
- Catalog Description
- An introduction to the theory of statistical learning and practical machine learning algorithms with applications in signal processing and data analysis.
- T. Hastie, R. Tibshirani, and J.H. Friedman, The Elements of Statistical Learning (2nd edition), Springer, 2009. ISBN 9780387848570(optional) (comment: Available for download with corrections in the 5th printing with course resources at:
SPIs are a subset of the abilities a student will be able to demonstrate upon successfully completing the course.
- Topical Outline
1. Supervised learning a) The Bayes classifier and the likelihood ratio test b) Nearest neighbor classification c) Linear classifiers i. plugin classifiers (LDA, logistic regression, Naive Bayes) ii. the perceptron learning algorithm iii. maximum margin principle and separating hyperplanes d) Linear regression i. least-squares linear regression ii. the LASSO e) Theory of generalization i. overfitting ii. concentration inequalities iii. VC dimension and generalization bounds iv. the bias-variance tradeoff v. regularization f) Nonlinear classifiers i. nonlinear feature maps ii. the kernel trick iii. SVMs iv. multi-layer neural networks g) Nonlinear methods in regression h) Error estimation and validation 2. Unsupervised learning a) Linear dimensionality reduction and principal component analysis b) Mutltidimensional scaling c) Clustering i. K-means ii. GMMs and the EM algorithm iii. spectral clustering d) Density estimation e) Feature selection f) Nonlinear dimensionality reduction (manifold learning) 3. Other topics (as time permits) a) Matrix factorizations b) Graphical models c) Ensemble methods (boosting, random forests) d) - Deep learning
© 2022 Georgia Institute of Technology