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Courses - Fall 2024
Machine Learning
Open Seats as of
07/19/2024 at 10:30 PM
Probability and Statistics
Credits: 3
Grad Meth: Reg
Prerequisite: Undergraduate courses in calculus and basic linear algebra.
Cross-listed with: DATA601, BIOI601.
Credit only granted for: BIOI601, DATA601 or MSML601.
Provides a solid understanding of the fundamental concepts of probability theory and statistics. The course covers the basic probabilistic concepts such as probability space, random variables and vectors, expectation, covariance, correlation, probability distribution functions, etc. Important classes of discrete and continuous random variables, their inter-relation, and relevance to applications are discussed. Conditional probabilities, the Bayes formula, and properties of jointly distributed random variables are covered. Limit theorems, which investigate the behavior of a sum of a large number of random variables, are discussed. The main concepts random processes are then introduced. The latter part of the course concerns the basic problems of mathematical statistics, in particular, point and interval estimation and hypothesis testing.
Principles of Data Science
Credits: 3
Grad Meth: Reg
Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning).
Cross-listed with: DATA602, BIOI602.
Credit only granted for: BIOI602, DATA602, MSML602 or CMSC641.
Formerly: CMSC641.
An introduction to the data science pipeline, i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of what data science means and systems and tools commonly used for data science, and illustrates the principles of data science through several case studies.
Principles of Machine Learning
Credits: 3
Grad Meth: Reg
Restriction: Must be in one of the following programs: (Data Science Post-Baccalaureate Certificate, Master of Professional Studies in Data Science and Analytics, or Master of Professional Studies in Machine Learning).
Cross-listed with: DATA603, BIOI603, MSQC603.
Credit only granted for: BIOI603, DATA603, MSML603, MSQC603 or CMSC643.
Formerly: CMSC643.
A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.
The lecture may be conducted online some weeks and in person other weeks. Please see ELMS for Class meeting details.
Natural Language Processing
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: DATA603 or MSML603.
Cross-listed with: DATA641.
Credit only granted for: DATA641 or MSML641.
Introduces fundamental concepts and techniques involved in getting computers to deal more intelligently with human language. Focused primarily on text (as opposed to speech), the class will offer a grounding in core NLP methods for text processing (such as lexical analysis, sequential tagging, syntactic parsing, semantic representations, text classification, unsupervised discovery of latent structure), key ideas in the application of deep learning to language tasks, and consideration of the role of language technology in modern society.
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: DATA603, MSML603, or MSQC603.
Machine learning can expand the capabilities of robotic systems including UAV, and applies to a variety of robotic system functions including planning, control, and perception. Robot Learning covers the application of learning techniques including Reinforcement Learning, Learning from Demonstration, Evolutionary, and Robot Shaping that may be used with a variety of machine learning paradigms. A variety of paradigms are available to generate models (e.g., CMAC, lazy learning, LWR, RBF, deep networks). These learning techniques and paradigms are then combined with traditional robotic control approaches (e.g., motor schema, behavior-based, direct and inverse methods) to create controllers to control the robots while operating in real-world environments. This course will explore applying machine learning techniques, paradigms, and control design to robotic systems including UAV. Students will construct a simulation environment for robot system by using machine learning methods.
Credits: 3
Grad Meth: Reg
Cross-listed with: DATA650.
Credit only granted for: MSML650 or DATA650.
Presents the state of the art in cloud computing technologies and applications. Topics will include: telecommunications needs, architectural models for cloud computing, cloud computing platforms and services. Data center networking, server, network and storage virtualization technologies, and containerization. Cloud operating and orchestration systems. Security, privacy, and trust management; resource allocation and quality of service; interoperability and internetworking.