Graduate Program

This course provides a hands-on introduction to widely-used tools for data science. Topics include Linux; languages and packages for statistical analysis and visualization; cluster and parallel computing including GPUs; Hadoop and Spark; libraries for machine learning; NoSQL databases; and cloud services.

PREREQUISITES: Programming experience strongly recommended.

Location

(MW 9:00AM - 10:15AM)

The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes.

Location

(MW 4:50PM - 6:05PM)

This course primarily focuses on algorithms for large-scale optimization problems arising in machine learning and data science applications. The first part will cover first-order methods including gradient and subgradient methods, mirror descent, proximal gradient method, accelerated gradient method, Frank-Wolfe method, and inexact proximal point methods. The second part will introduce algorithms for nonconvex optimization, stochastic optimization, distributed optimization, manifold optimization, reinforcement learning, and those beyond first-order.

Location

(TR 9:40AM - 10:55AM)

Fundamental concepts and techniques of data mining, including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics include outlier detection, stream mining, and social media data mining. CSC 440, a graduate-level course, requires additional readings and a course project.

Location

(TR 2:00PM - 3:15PM)

This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.

Location

(MW 12:30PM - 1:45PM)

This course will cover foundational concepts in descriptive analyses, probability, and statistical inference.  Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables,  elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment. 
 

Location

(TR 4:50PM - 6:05PM)

The course provides an introduction to modern machine learning concepts, techniques, and algorithms. Topics discussed include regression, clustering and classification, kernels, support vector machines, feature selection, goodness of fit, neural networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets. Students will be expected to work with Python programming environment to complete the assignments. 
PRE-REQUISITES:  DSCC/CSC 462 or equivalent introductory statistics background.

Location

(MW 3:25PM - 4:40PM)

Description:  Time series analysis is a valuable data analysis technique in a variety of industrial (e.g., prognostics and health management), business (e.g., financial data analysis) and healthcare (e.g., disease progression modeling) applications. Moreover, forecasting in time series is an essential component of predictive analytics. The course will begin with an introduction to practical aspects relevant to time series data analysis such as data collection, characterization, and preprocessing. Topics covered will include smoothing methods (moving average, exponential smoothing), trend and seasonality in regression models, autocorrelation, AR and ARIMA models applied to time series data. Deep learning models including feedforward, recurrent, gated and convolutional architectures will also be studied. Students shall work on projects with time-series data sets using modeling tools in Python.
 

Location

(TR 11:05AM - 12:20PM)

Topics include introduction to Python programming and data structures relevant to healthcare data. The course will also provide a hands-on introduction to widely used tools for data science, languages and packages used for statistical analysis and visualization; parallel computing and Spark; libraries for machine learning and deep learning; databases including NoSQL; and cloud services.

Location

(W 6:15PM - 8:45PM)

The course covers the essentials of the statistical foundations for interpretation and visualization of data. Using statistical tools, gain an understanding of how to interpret data quantitatively and to explore data-oriented structures. The primary focus is on descriptive statistics used to present and summarize numerical information. The course also emphasizes the design of systems for data visualization and related best practices for use in a healthcare setting. Course projects and assignments will involve accessing public healthcare data sources (e.g. MIMIC-III) or a deidentified data repository to extract relevant patient level attributes and create dashboards and visualizations to practice data storytelling for relevant healthcare problems. Students will be introduced to the R programming language which is a mainstay in statistical computing.

Location

(M 6:15PM - 8:45PM)

The capstone/practicum provides an experience for data science majors/MS candidates to apply the core knowledge and skills attained during their program to a tangible data science focused project. Students will work in small teams on a project that applies data science methods to the analysis of a real-world problem. The instructor will guide each team in developing a topic that makes use of the knowledge the team members gained through their application area courses. The identified projects or problems and data sets will cover a range of application areas and reflect real-world needs from industry, medicine and government. Each student will be required to write a paper about their project, which satisfies one upper-level writing requirement for majors and Plan B for master's.

PREREQUISITES: DSCC 440 (Data Mining) AND an introductory statistics course such as DSCC 462. DSC 461 (Database Systems) strongly recommended prior but may be taken concurrently. FOR DSC GRADUATING SENIORS and MS CANDIDATES. 

GRADUATING STUDENTS this semester have priority for eligibility/instructor permission.

PERMISSION REQUEST: To seek instructor permission/eligibility, follow directions on UR Student.
https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf

Location

(MW 10:25AM - 11:40AM)

This course is for master's students that have made arrangements with a faculty member to complete readings and discussion in a particular subject in their field of study.

Location

( 7:00PM - 7:00PM)

This course is for master's students that have made arrangements with a faculty member to complete readings and discussion in a particular subject in their field of study.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This seminar offers an introduction to Large Language Models (LLMs), covering essential concepts such as Transformers, BERT, GPT-3, InstructGPT, prompting & decoding, and emergent abilities. Students will engage with a range of topics through paper presentations on themes such as Tool-Augmented LLMs, Multimodal Learning, LLMs for Science, Social and Ethical Concerns, Superintelligence Concerns, and Democratizing LLMs. Participants are required to present and discuss papers, write critical literature reviews, reproduce paper results, and collaborate on team projects. This seminar aims to provide a thorough understanding of LLMs, exploring their origins, opportunities, and concerns to enhance professional expertise in the field.Prerequisites: Students have completed at least one course in Natural Language Processing (NLP), Deep Learning (DL), or Machine Learning (ML), such as CSC 2/447 (Natural Language Processing), CSC 2/445 (Deep Learning), CSC 2/466 (Frontiers in Deep Learning), CSC 2/477 (End-to-End Deep Learning), and CSC 2/446 (Machine Learning). Other relevant courses in NLP, DL, or ML are also acceptable.

Location

(MW 9:00AM - 10:15AM)

This course is designed for master's degree students who have completed all required coursework but still need to finalize specific degree requirements under less than half-time enrollment.

Location

( 7:00PM - 7:00PM)

This course provides master's students who are currently completing their final required coursework, or with special circumstances like an approved reduced courseload, with the opportunity to work full-time on their degrees. Students will make significant progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)

This course provides master’s students who have completed or are currently completing all course requirements with the opportunity to work full-time on their thesis. Students will make significant progress toward completing their degrees.

Location

( 7:00PM - 7:00PM)