Machine Learning

COMP SCI 761: Mathematical Foundations Of Machine Learning
Spring 2021

The course covers advanced theory and methods in machine learning, including probabilistic modeling, hypothesis testing, classification and regression,... maximum likelihood and Bayesian inference, PAC learning and VC theory, nonparametric methods, and state-of-the-art machine learning algorithms.


COMP SCI 760: Introduction to Machine Learning
Spring 2020

The course provides an introduction to the theory and practical methods for machine learning, and ... is designed to give a graduate-level student a thorough grounding in the methodologies, mathematics, and algorithms of machine learning. Topics covered include nearest neighbor method, decision tree learning, Support Vector Machines, Bayesian networks, neural networks, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, mistake bounds, etc.


COMP SCI 540: Introcution to Artificial Intelligence
Summer 2019

The course focuses on principles of knowledge-based search techniques, automatic deduction, knowledge... representation using predicate logic, machine learning, probabilistic reasoning. Applications in tasks such as problem solving, data mining, game playing, natural language understanding, computer vision, speech recognition, and robotics.


COMP SCI 532: Matrix Methods in Machine Learning
Fall 2020

This course is an introduction to machine learning that focuses on matrix methods and features real-world applications ranging from classification and clustering to denoising and data analysis. Mathematical topics ...covered include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms.


LIS 706: Data Mining
Fall 2019

The course introduces stages of a data mining project, organizational data audits, metadata and data management... concepts, data preparation techniques, data project evaluation and principles of data ethics. Students learn and apply introductory data mining tools and techniques for data clustering, dividing data into classes, making predictions, and identifying networks.


Data Structures

COMP SCI 577: Introduction to Algorithms
Fall 2019

The main focus of this course lies on techniques for constructing correct efficient algorithms and on tools... to reason about them. Design paradigms include greed, divide-and-conquer, dynamic programming, reductions, and randomness (time permitting). A second focus point is computational intractability. NP-complete problems are covered, as well as ways to deal with them.


COMP SCI 400: Programming III
Fall 2018

The course introduces balanced search trees, graphs, graph traversal algorithms, hash tables and sets, and... complexity analysis and about classes of problems that require each data type.


COMP SCI 300: Programming II
Summer 2018

This is a Java programming course covers principles and practices of Object Oriented (OO) programming, analysis and ...design, and fundamental concepts in advanced data structures.

COMP SCI 320: Data Programming II
Spring 2020

This is a Python programming course.


Biomedical Informatics

BMI 776: Advanced Bioinformatics
Spring 2021

The primary goal of this course is to teach algorithms for problems such as: modeling sequence classes and features, multiomics analysis, gene discovery, ... network biology, applied machine learning, and single-cell genomic analysis. This class provides students with a strong background for conducting their own bioinformatics research.


COMP SCI 576: Introduction to Bioinformatics
Fall 2020

The primary goal of the course was to teach algorithms for analyzing genomes, RNA, proteins, and ...biological networks. These techniques will provide students a strong background for conducting their own bioinformatics research.


Database Systems

COMP SCI 839: Modern Data Management and Machine Learning Systems
Spring 2020

The course covered the latest trends in modern data management and machine learning systems designs to better support the next generation of ML applications, and applications of ML to optimize ...the architecture and the performance of data management systems.


COMP SCI 764: Topics in Database Management Systems
Fall 2020 & Fall 2019

The course covers a number of advanced topics in the development of database management systems (DBMS) and the modern applications of databases. The topics discussed include advanced ...concurrency control and recovery, query processing and optimization, advanced access methods, parallel and distributed data systems, extensible data systems, implications of cloud computing for data platforms, and data analysis on large datasets.


COMP SCI 564: Database Management Systems; Design and Implementation
Summer 2019

The course introduces the concept of a data model, the entity-relationship (ER) model, the relational model, and learn how to use the SQL query language. The logical and physical database design issues ...were also covered. The other half of the course concentrates on DBMS implementation. The course covers file organization, various indexing methods, techniques for external sorting, and how a DBMS implements a relational operator, and the basics of query optimization.


Data Analytics

LIS 803: Computational Research Methods
Spring 2018

The course focuses on formulating and investigating novel questions with tools from data mining and learning analytics including social network analysis, natural language processing, Markov modeling, Bayesian inference, and agent-based modeling.


COMP SCI 765: Data Visualization
Fall 2020

The course covers: principles of the visual presentation of data; survey information visualization, scientific visualization, and ... visual analytics; design and evaluation of visualizations and interactive exploration tools; introduction to relevant foundations in visual design, human perception, and data analysis; encodings, layout and interaction; approaches to large data sets; visualization of complex data types such as scalar fields, graphs, sets, texts, and multi-variate data.


ED PSYCH 761: Statistical Methods II
Spring 2017

The course covers: analysis of variance and covariance, multiple linear regression; chi-square and various nonparametric techniques.


ED PSYCH 760: Statistical Methods I
Fall 2016

The course covers: introductory descriptive statistics and statistical inference; measures of central tendency and variability, confidence intervals, theory of hypothesis testing, correlation techniques.


ISYE 601: Research Methods for Healthcare Systems
Spring 2017

The course covers: information gathering methods, qualitative, quantitative, and mixed methods approaches, and various Human Factors Engineering methods for task/process/system description and analysis.