CSE Summer 2021 Courses

NOTE: This listing represents our current plan for the semester in question. Course offerings and class times are occasionally subject to change for reasons beyond our control.

SUMMER SESSION I (5/25/21-7/1/21)

CSE 003 INTRODUCTION TO PROGRAMMING PART A, MTWR 12:00-1:35 (Remote Synchronous),  Professor Kallie Ziltz

Covers the same material as the first half of CSE 007. Designed to allow more class and laboratory time for each topic. No prior programming experience needed. Cannot be taken by students who have completed CSE 007.


CSE 017 PROGRAMMING AND DATA STRUCTURES, MTWR 12:00-1:35 (Remote Synchronous), Professor Arielle Carr

This course is a programming-intensive exploration of software design concepts and implementation techniques. It builds on the student's existing knowledge of fundamental programming. Topics include object-oriented software design, problem-solving strategies, algorithm development, and classic data structures. Prerequisite: CSE 004 or CSE 007 or (CSE 002 and (CSE 001 or CSE 012 or ENGR 010))


CSE 140 FOUNDATIONS OF DISCRETE STRUCTURES & ALGORITHMS, MTWR 10:00-11:35 (Remote Synchronous), Professor Ahmed Hassan

Basic representations used in algorithms: propositional and predicate logic, set operations and functions, relations and their representations, matrices and their representations, graphs and their representations, trees and their representations. Basic formalizations for proving algorithm correctness: logical consequences, induction, structural induction. Basic formalizations for algorithm analysis: counting, pigeonhole principle, permutations. Prerequisites: (MATH 021 or MATH 031 or MATH 051 or MATH 076) and CSE 017 (co-requisite).


CSE 202 COMPUTER ORGANIZATION AND ARCHITECTURE, MTWR 10:00-11:35(Remote Synchronous), Professor Mark Erle

Interaction between low-level computer architectural properties and high-level program behaviors: instruction set design; digital logics and assembly language; processor organization; the memory hierarchy; multicore and GPU architectures; and processor interrupt/exception models.  Credit will not be given for both CSE 201 and CSE 202. Prerequisite: CSE 17


CSE 271 PROGRAMMING IN THE C AND UNIX ENVIRONMENT, MTWR 2:00-3:35 (Remote Synchronous), Professor Mark Erle

Programming in Unix and Windows - Students learn Unix and Windows operating system fundamentals including features, organization, and process management. Emphasis is placed on Unix's BASh and Window's PowerShell scripting languages. Tools commonly available with these operating systems, such as those for editing, compiling, debugging, scheduling jobs, etc., are also explored. Students should expect to write a variety of small programming assignments. Prerequisite: CSE 109


SUMMER SESSION II (7/6/21-8/12/21)

CSE 004 INTRODUCTION TO PROGRAMMING PART B, MTWR 12:00-1:35 (Remote Synchronous),  Professor Kallie Ziltz

Covers the same material as the second half of CSE 007. Designed to allow more class and laboratory time for each topic. Cannot be taken by students who have completed CSE 007.


CSE 109 SYSTEMS SOFTWARE, (Remote Asynchronous) Professor Corey Montella

Advanced programming and data structures, including dynamic structures, memory allocation, data organization, symbol tables, hash tables, B-trees, data files. Object-oriented design and implementation of simple assemblers, loaders, interpreters, compilers and translators. Practical methods for implementing medium-scale programs. Prerequisite: CSE 17


CSE 298 FOUNDATIONS OF ROBOTICS (Remote Asynchronous), Professor Corey Montella

Introduces students to the field of robotics, covering foundational mathematics and physics as well as important algorithms and tools. Topics include simulation, kinematics, control, machine learning, and probabilistic inference. The mathematical basis of each area will be covered, followed by practical application to common robotics tasks. This course is designed to be taught remotely using simulated robot platforms and sensors. Pre-requisites:  CSE 004 OR CSE 007 OR (CSE 002 AND (CSE 001OR CSE 012 OR ENGR 010 ) )


CSE Fall 2021 Courses

NOTE: This listing represents our current plan for the semester in question. Course offerings and class times are occasionally subject to change for reasons beyond our control. Courses will be offered on campus unless listed otherwise.


CSE 003 INTRO. TO PROGRAMMING PART A, MWF 12:10-1:00, Professor Brian Chen

Covers the same material as the first half of CSE 007. Designed to allow more class and laboratory time for each topic. No prior programming experience needed. Cannot be taken by students who have completed CSE 007.


CSE 004 INTRODUCTION TO PROGRAMMING PART B, MWF 10:45-12:00, Professor Kallie Ziltz

Covers the same material as the second half of CSE 007. Designed to allow more class and laboratory time for each topic. Cannot be taken by students who have completed CSE 007.


CSE 007 INTRODUCTION TO PROGRAMMING

Problem-solving using the Java programming language. Data types, control flow, methods, arrays, objects, inheritance, breadth of computing. Includes laboratory. If credit is given for CSE 007 then no credit will be given for CSE 003 nor CSE 004.

CSE 007-010, MWF 10:45-12:00, Professor Sharon Kalafut

CSE 007-011, MWF 9:20-10:35, Professor Kallie Ziltz
CSE 007-013, MWF 1:35-2:50, Professor Kallie Ziltz


CSE 012-011 SURVEY OF COMPUTER SCIENCE, MW 1:35-2:50, Professor Sharon Kalafut

This course provides a project-based exploration of fundamental concepts in computing and "computational thinking." Topics include but are not limited to networks, data visualization, information storage and retrieval, and the popular Python programming language. Each project presents applications of computing in solving real life problems. In this course you will learn to write Python code to visualize data from different sources. You will learn how information is transferred across networks and how to store and retrieve data from relational database management systems. Optional Structured Study Groups will be provided for students who express interest.


CSE 017 PROGRAMMING & DATA STRUCTURES

This course is a programming-intensive exploration of software design concepts and implementation techniques. It builds on the student's existing knowledge of fundamental programming. Topics include object-oriented software design, problem-solving strategies, algorithm development, and classic data structures.

CSE 017-010, TR 1:35-2:50, Professor Houria Oudghiri

CSE 017-011, TR 3:00-4:15, Professor Houria Oudghiri

CSE 017-012, TR 7:55-9:10, Professor Houria Oudghiri


CSE 109 SYSTEMS SOFTWARE

Advanced programming and data structures, including dynamic structures, memory allocation, data organization, symbol tables, hash tables, B-trees, data files. Object-oriented design and implementation of simple assemblers, loaders, interpreters, compilers and translators. Practical methods for implementing medium-scale programs.

CSE 109-011, MWF 10:45-12:00, Professor Mark Erle

CSE 109-012, MWF 3:00-4:15, Professor Mark Erle


CSE 140 FOUNDATIONS OF DISCRETE STRUCTURES & ALGORITHMS, TR 3:00-4:15, Professor Ahmed Hassan

Basic representations used in algorithms: propositional and predicate logic, set operations and functions, relations and their representations, matrices and their representations, graphs and their representations, trees and their representations. Basic formalizations for proving algorithm correctness: logical consequences, induction, structural induction. Basic formalizations for algorithm analysis: counting, pigeonhole principle, permutations. Prerequisites: (MATH 021 or MATH 031 or MATH 051 or MATH 076) and CSE 017 (co-requisite).


CSE 160 INTRO TO DATA SCIENCE

Interested in understanding the hype about data science, big data, or data analytics? This course introduces you to data science, a fast-growing and interdisciplinary field, focusing on the computational analysis of data to extract knowledge and insight. You will be introduced to the collection, preparation, analysis, modeling, and visualization of data, covering both conceptual and practical issues. Applications of data science across multiple fields are presented, and hands-on use of statistical and data manipulation software is included. The course is open to students from all areas of study; the only prerequisite is some programming experience (automatic if you've taken CSE 1, 2, 12, or BIS 335, or permission of the instructor is available if you can show that you've successfully completed a programming course online, in high school, or elsewhere).

CSE 160-010, Lecture Remote Asynchronous  Recitation M 1:35-2:50, Professor Brian Davison

CSE 160-011, Lecture Remote Asynchronous  Recitation W 1:35-2:50, Professor Brian Davison


CSE 216 SOFTWARE ENGINEERING

The software life-cycle; life-cycle models; software planning; testing; specification methods; maintenance. Emphasis on team work and large-scale software systems, including oral presentations and written reports.

CSE 216-010, TR 1:35-2:50, Stephen Urban

CSE 216-011, MW 12:10-1:25, Professor Mark Erle

CSE 216-011, MW 12:10-1:25, Professor Stephen Urban


CSE 241 Data Base Systems and Applications, MW 7:55-9:10, Professor Sihong Xie

Design of large databases: Integration of databases and applications using SQL and JDBC; transaction processing; performance tuning; data mining and data warehouses. Not available to students who have credit for CSE 341 or IE 224.


CSE 252 COMPUTERS, INTERNET AND SOCIETY

An interactive exploration of the current and future role of computers, the Internet, and related technologies in changing the standard of living, work environments, society and its ethical values. Privacy, security, depersonalization, responsibility, and professional ethics; the role of computer and Internet technologies in changing education, business modalities, collaboration mechanisms, and everyday life. (SS).

CSE 252-010, Lecture Remote Asynchronous  Recitation T 2:05-3:20 (Building C) , Professor Eric Baumer

CSE 252-011, Lecture Remote Asynchronous  Recitation M 10:45-12:00, Professor Eric Baumer


CSE 262 PROGRAMMING LANGUAGES

Use, structure and implementation of several programming languages.

CSE 262-010, MWF 12:10-1:00, Professor Corey Montella

CSE 262-011 MWF 1:35-2:25, Professor Corey Montella


CSE 264 WEB SYSTEMS PROGRAMMING, TR 9:20-10:35, Professor Dominic DiFranzo

Practical experience in designing and implementing modern Web applications. Concepts, tools, and techniques, including: HTTP, HTML, CSS, DOM, JavaScript, Ajax, PHP, graphic design principles, mobile web development. Not available to students who have credit for IE 275.


CSE 281 CAPSTONE PROJECT II, TR 3:00-4:15, Professors Corey Montella, George Witmer

Second of a two semester capstone course sequence that involves the design, implementation, and evaluation of a computer science software project; conducted by small student teams working from project definition to final documentation; each student team has a CSE faculty member serving as its advisor; The second semester emphasis is on project implementation, verification & validation, and documentation requirements. It culminates in a public presentation and live demonstration to external judges as well as CSE faculty and students. Prerequisite: Senior standing and CSE 280.


CSE 297 BLOCKCHAIN ALGORITHMS & SYSTEMS, TR 10:45-12:00, Professor Hank Korth

After an introduction to the concepts of cryptocurrencies and blockchains, the course will focus on the data structures, algorithms, and mathematics that make blockchains secure, tamper-resistant, and structured in a highly distributed manner.  This course counts as a technical elective for CS majors and will count towards the CS minor.  CSB students may count this as "CSE elective from approved list" or as a professional elective (but not both).  The course will have several graded coding assignments, quizzes, and a final.  Students who took CSB 242 may find the first 2-3 weeks of CSE 297 to have some small overlap, but they are free to register for CSE 297. Prerequisites: CSE 109 or CSE 241 or CSE 341. Can take CSE 109 concurrently.


CSE 303 OPERATING SYSTEM DESIGN

Process and thread programming models, management, and scheduling. Resource sharing and deadlocks. Memory management, including virtual memory and page replacement strategies. I/O issues in the operating system. File system implementation. Multiprocessing. Computer security as it impacts the operating system.

CSE 303-010, MW 1:35-2:50, Professor Michael Spear

CSE 303-011, MW 3:00-4:15, Professor Ahmed Hassan


CSE 320/420 BIOMEDICAL IMAGE COMPUTING & MODELING, TR 8:25-9:40, Professor Lifang He

Biomedical image modalities, image computing techniques, and imaging informatics systems. Understanding, using, and developing algorithms and software to analyze biomedical image data and extract useful quantitative information: Biomedical image modalities and formats; image processing and analysis; geometric and statistical modeling; image informatics systems in biomedicine. Credit will not be given for both CSE 320 and CSE 420.


CSE 326/426 MACHINE LEARNING, MW 1:35-2:50, Professor Sihong Xie

** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

Bayesian decision theory and the design of parametric and nonparametric classification and regression: linear, quadratic, nearest-neighbors, neural nets. Boosting, bagging. Credit will not be given for both CSE 326 and CSE 426


CSE 340 DESIGN & ANALYSIS OF ALGORITHMS

Algorithms for searching, sorting, manipulating graphs and trees, finding shortest paths and minimum spanning trees, scheduling tasks, etc.: proofs of their correctness and analysis of their asymptotic runtime and memory demands. Designing algorithms: recursion, divide-and-conquer, greediness, dynamic programming. Limits on algorithm efficiency using elementary NP-completeness theory.

CSE 340-010, MW 12:10-1:25, Professor Lichao Sun

CSE 340-011, MW 3:00-4:15, Professor David Saldana



CSE 371/CSE 471 PRINCIPLES OF MOBILE COMPUTING, MW 9:20-10:35, Professor Mooi Choo Chuah  ** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

Lecture/seminar course covering the fundamental concepts and technology underlying mobile computing and its application as well as current research in these areas. Examples drawn from a variety of application domains such as health monitoring, energy management, commerce, and travel. Issues of system efficiency will be studied, including efficient handling of large data such as images and effective use of cloud storage. Research coverage will be drawn from the best publications in the recent research conferences. Deep learning methods will be covered. Students will do Android programming and possibly develop Alexa/Google home skills for homework assignments and final class projects. Prerequisites: CSE 109 and (CSE 202 or ECE 201).


CSE 375/475 PRINCIPLES & PRACTICE OF PARALLEL COMPUTING, MW 9:50-111:05, Professor Roberto Palmieri  ** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

It's the era of data, and having knowledge on how to design and develop correct high performance algorithms and applications for computing data is a fundamental requirement for prospective successful software engineers and designers. CSE-375/475 focuses on that, covering both theoretical and practical aspects, providing students with the sufficient knowledge to implement and reason about parallel applications. A particular focus is given to concurrency, which often represents a barrier for many developers given its complexity in providing correct computation due to the presence of simultaneous accesses on shared data. In this regard, the course covers the traditional lock-based programming, and also state-of-the-art (software and hardware) solutions to code concurrent applications without expositing locks to programmer.


CSE 398/498 BIG DATA ANALYTICS, MW 2:05-3:20, Professor Daniel Lopresti

** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

In this 3-credit project course, we will gain a practical working knowledge of large- scale data analysis using the popular open source Apache Spark framework. Spark provides a powerful model for distributing programs across clusters of machines and elegantly supports patterns that are commonly employed in big data analytics, including classification, collaborative filtering, and anomaly detection, among others.

Beyond the textbook, supplemental readings will provide additional background for each application area. During class, students will take turns helping to lead the discussion. A final project will be required.

Enrollment in this course is limited and requires permission of the instructor. Please note that this is not a basic course on data mining, machine learning, or distributed computing; it assumes you already know something about these topics and/or you can pick up the necessary details on your own. Contact the instructor, Prof. Daniel Lopresti, for details. This course will be taught in Building C.


CSE 398/498 COMPUTER VISION, TR 9:20-10:35, Professor Aparna Bharati

** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

Computer Vision is the process of enabling machines (computers) to see and understand the world around us. With the recent growth in the usage of images and videos for day-to-day interaction and sensitive applications of security such as face recognition and autonomous driving, the field has become extremely important and popular. It provides us with the knowledge and tools for automated visual interpretation. This course aims to provide an introduction to how images are represented in a computer, how it can be processed to highlight certain aspects of it, and how we can extract meaningful information from them for the purposes of image matching, recognition, and reconstruction. We will cover topics such as image formation and acquisition, scene geometry, image processing (filtering and segmentation), feature extraction (handcrafted and learning-based) and matching, and classification techniques. Students will learn techniques from signal processing, pattern recognition, machine learning and deep learning that are dominantly used to process and understand visual data. The course will enable the students to build full pipelines for solutions to some classic vision problems and familiarize them with some new and challenging problem statements in the field.

Required: basic skills in Python and Matlab programming. Good to have: basics in image and signal processing, linear algebra, geometry and optics, and basic experience with OpenCV; these will help in faster adoption of the topics discussed in class and will make assignments easier.


CSE 398/498 Iterative Methods for Large Sparse Linear Systems, MW 10:45-12:00, Professor Arielle Carr

** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

In this course, we discuss commonly used iterative methods for efficiently solving large, sparse linear systems. We cover basic iterative methods, Krylov methods, preconditioning, and multigrid. We analyze the theoretical properties of these methods, including convergence behavior, and evaluate performance using numerical experimentation for various real-world applications.  Time permitting, we extend our discussion to iterative solutions of eigenvalue problems. Students should have a basic knowledge of linear algebra, in particular matrix and vector manipulation, and computer programming.  The use of MATLAB will be required. 

The graduate version of this course (498) will require students to apply numerical experiments in assignments and projects to their own research problems.  The instructor will supply such additional problems if students’ research does not naturally lend itself to this course.


CSE 398/498 Data Science for Smart Cities, TR 1:35-2:50, Professor Yu Yang

** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

 Empowered by rich data collected from various infrastructures in our cities and machine learning techniques, our cities are becoming “smarter”. In this course, we discuss how data science is used to innovate our cities. We cover topics such as urban sensing, data-driven modeling and analytics for smart city services, data-driven decision making, data visualization, and novel applications in various city phenomena. Students are expected to (i) read and present research papers drawn from top conferences, (ii) participate in discussions of the papers, and (iii) design, implement and present their ideas for the final class project. Prerequisites: CSE 017 and (CSE 160 or CSE 326).


CSE 401/ECE 401 ADVANCED COMPUTER ARCHITECTURE, TR 10:45-12:00, Professor Jieming Yin

Design, analysis and performance of computer architectures; high-speed memory systems; cache design and analysis; modeling cache performance; principle of pipeline processing, performance of pipelined computers; scheduling and control of a pipeline; classification of parallel architectures; systolic and data flow architectures; multiprocessor performance; multiprocessor interconnections and cache coherence.


CSE 406 RESEARCH METHODS, TR 9:50-11:05, Professor Jeff Heflin

Technical writing, reading the literature critically, analyzing and presenting data, conducting research, making effective presentations, and understanding social and ethical responsibilities. Topics drawn from probability and statistics, use of scripting languages, and conducting large-scale experiments. Must have first-year status in CS program.


CSE 411 ADVANCED PROGRAMING TECHNIQUES, TR 8:25-9:40, Ahmed Hassan

** Offered as Flex.  There is a course section for students attending remote and a course section for students attending in the classroom.**

Deeper study of programming and software engineering techniques. The majority of assignments involve programming in contemporary programming languages. Topics include memory management, GUI design, testing, refactoring, and writing secure code.


CSB COURSES – FALL 2021

CSB 304 TECHNOLOGY & SOFTWARE VENTURES, TR 12:10-1:25, Professor Joshua Ehrig

Designed from the perspective of a functional leader, this course provides students with a holistic perspective of developing a successful software venture in an interdisciplinary and experiential environment. Students will develop a software-oriented idea concurrent with module delivery that will contain best practices, case studies, and subject-matter experts. Examination will include business model fundamentals, customer discovery, translating requirements to a minimum viable product, agile development, user acquisition, and traction. Prior programming experience preferred, but, not required. Open to any major.


CSB 313 DESIGN OF INTEGRATED BUSINESS APPLICATIONS II, TR 3:00-4:15,  Professors Brian Colville, Debra Kreider, Andrea Smith, George Witmer

Integrated Product Development (IPD) Capstone I. Industry-based business information systems design project. Information systems design methodology, user needs analysis, project feasibility analysis of design alternatives, and integrated product development methodology. Formal oral and written presentations to clients. Click here for official description.


This listing represents our current plan for the semester in question. Course offerings and class times are occasionally subject to change for reasons beyond our control.