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: CSC 161, CSC 171 or some equivalent programming experience strongly recommended.

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

Wegmans Room 1400 (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. Prerequisites: ECE 242 or equivalent

Location

Gavett Hall Room 202 (MW 4:50PM - 6:05PM)

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

Gavett Hall Room 202 (TR 3:25PM - 4:40PM)

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

Harkness Room 115 (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. 

PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 (or equivalent discrete math and calculus coursework)

Location

Gavett Hall Room 206 (TR 4:50PM - 6:05PM)

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.

PREREQUISITES: Introductory Statistics (DSC 262/STT212/STT213 or equivalent), Linear Algebra and Differential equations (MTH 165 or equivalent), and applied Python programming (CSC161 or equivalent)

Location

Dewey Room 2162 (TR 11:05AM - 12:20PM)

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: DSC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 262/462, STT212 or STT213 or equivalent. DSC 261/461 (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

Gavett Hall Room 206 (MW 10:25AM - 11:40AM)

The course will involve readings from current literature on machine learning and artificial intelligence applications for medical data analytics.

Meetings every other week and presentation at the end of the semester.

Internship must have approval of ASE graduate practical research internship.

Blank Description

Notify advisor before enrolling

Patients sleep poorly in the hospital; disrupted and insufficient sleep can affect how well and how quickly patients recover, as well as negatively impacting their overall experience of care. This project will build on work last semester to identify and characterize sleep disruptions documented in the electronic health record. Using survey data collected over the last 12 months, analyze the relationship between quantitative (EHR-based) indicators of poor sleep and patients' self-reported qualitative experience of sleep in order to begin to identify what kinds or patterns of interruptions are especially impactful to patients.

Course evaluation will be based on:

(1) produce and submit at least one conference abstract;

(2) present her work to the Health Lab at the end of the semester;

(3) attend weekly meetings and provide progress reports. 

Causal reasoning, the ability to understand the connection between outcome and its antecedent, is of critical importance to children's future social life and academic achievement (Reed et al., 2015). This project will (1) conduct the literature review on the dimensions of casual reasoning, children's developmental trajectory in causal reasoning, previous intervention on simulating causal reasoning; (2) utilize ROCStories dataset and design a study to collect children's utterances between peers in storytelling; (3) if applicable, work with the team to design a human-inspired AI system and thus promote both children and AI's capability in causal reasoning.

Reference:
Reed, H. C., Hurks, P. P. M., Kirschner, P. A., & Jolles, J. (2015). Preschoolers’ Causal Reasoning During Shared Picture Book Storytelling: A Cross-Case Comparison Descriptive Study. Journal of Research in Childhood Education, 29(3), 367–389. https://doi.org/10.1080/02568543.2015.1042126

Grade will be based on weekly meetings and project updates and a final report.

SELFIES is a surjective map between tokens and molecular graph. In this project, we are going to propose a small molecule sequence design pipeline which is able to propose new molecule to try for any purposes with low existing data. We do this by first training a language model for SELFIES using RoBERTa , then using this pre-trained model with deep ensemble neural networks to guide the small molecule drug design using Bayesian optimization.

Basis for evaluation: Meet 2 times/week: Give 2 group meetings and a final report.

Art-ness evaluation (i.e., to evaluate which image is more like art) is a novel topic in computational aesthetics which hasn’t been carefully studied. To this end, this project aims to develop a pipeline that can automatically compare the art-ness for a given batch of images. Hopefully, this evaluator could help to retrieve AI generated images that most resemble fine art paintings, or serve as a metric to assess the quality of visual art created by generative models. 

The student will give progress reports weekly to the advisor or assigned PhD students, and be receptive to feedback. The student needs to give a final project presentation in the group meeting, and generate a paper aiming at CVPR 2023 before the paper submission deadline.

The course will focus on investigation of current literature on machine learning and artificial intelligence applications for emotion recognition and exploration/collection of image and audio data for the empirical analysis emotions both as a cause and as an outcome. The course is designed to eventually help all parties involved to produce a paper in the area of emotion recognition. The research will help expand the literature in the areas of computational social science (at large), and political communication.

Course evaluation will be based on:

(1) The compilation of datasets that will be used for empirical analysis (i)

(2) the compilation of literature review related to emotion recognition for audio and video data (ii),

(3) evaluation of code and algorithms for emotion recognition (iii),

(4) attendance to weekly meetings and the submission of progress reports (iv).

Blank Description

Please see advisor before enrolling.

see advisor before enrolling