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Dec 26, 2024
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CS 6830 - Machine Learning
Machine Learning is concerned with the design and analysis of algorithms that enable computers to automatically find patterns in the data. This introductory course will give an overview of the main concepts, techniques and algorithms that are relevant for the theory and practice of machine learning. The course will cover the fundamental topics of classification, regression and clustering, starting with simple learning models such as perceptrons, decision trees and logistic regression, and ending with more advanced models including Support Vector Machines, Conditional Random Fields and Bayesian networks. The description of the formal properties of the algorithms will be supplemented with motivating applications in a wide range of areas including natural language processing, computer vision, bioinformatics and music analysis.
Requisites:
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes:
- Given a machine learning problem, students will be able to determine which of the studied algorithms is most appropriate.
- Students will be able to create new kernel functions and prove that particular functions satisfy kernel properties.
- Students will be able to implement simple learning algorithms such as kernel perceptrons and nearest neighbors.
- Students will be able to implement wrapper and filter methods for feature selection.
- Students will gain a basic understanding of sequential and statistical relational learning.
- Students will gain an understanding of classification with discriminative functions, probabilistic generative models and probabilistic discriminative models.
- Students will gain an understanding of major types of supervised learning, including regression and classification.
- Students will gain an understanding of supervised, unsupervised, and semi-supervised learning.
- Students will gain an understanding of the basic properties of Bayesian Networks.
- Students will gain an understanding of the kernel trick.
- Students will gain an understanding of the major principles of minimum squared error, maximum likelihood, maximum margin, and maximum entropy.
- Students will understand how to represent instances in machine learning as vectors of categorical or real-valued features.
- Students will understand major machine learning evaluation settings, including k-fold cross-validation.
- Students will understand the concept of overfitting and methods for reducing overfitting, such as quadratic regularization.
- Students will understand the importance of inductive bias in machine learning.
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