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Computer and Data Sciences Courses


CSDS 101: Introduction to Computer and Data Sciences

For students who want to explore the history, the current state, and future challenges of computer and data sciences. Topics include how computers work, computational thinking, how software development differs from traditional manufacturing, the Internet and World Wide Web, social networks, data collection, search engines and data mining, machine learning, trends in computer crime, security, and privacy, how technology is changing our laws and culture. The class includes a lab component where students will learn the Python programming language and other technologies and applications in order to further explore these topics. The recommended prerequisite is comfort with high school algebra.

Please reach out to the instructor of this course to confirm modality, or check the course list in SIS

Dates: June 1 - July 27, 2021

Session: 8 Week Session

Time: MR 9:00-11:50; TF 9:00-11:00

Instructor: Harold Connamacher

Credits: 4 credits

Departments: Computer and Data Sciences, New 2021 Summer

CSDS 233: Introduction to Data Structures

Different representations of data: lists, stacks and queues, trees, graphs, and files. Manipulation of data: searching and sorting, hashing, recursion and higher order functions. Abstract data types, templating, and the separation of interface and implementation. Introduction to asymptotic analysis. The Java language is used to illustrate the concepts and as an implementation vehicle throughout the course.
Offered as CSDS 233 and ECSE 233.

Please reach out to the instructor of this course to confirm modality, or check the course list in SIS

Dates: June 1 - July 27, 2021

Session: 8 Week Session

Time: MWF 9:30-12:25

Instructor: Erman Ayday

Credits: 4 credits

Departments: Computer and Data Sciences, New 2021 Summer

CSDS 302: Discrete Mathematics

A general introduction to basic mathematical terminology and the techniques of abstract mathematics in the context of discrete mathematics. Topics introduced are mathematical reasoning, Boolean connectives, deduction, mathematical induction, sets, functions and relations, algorithms, graphs, combinatorial reasoning.
Offered as CSDS 302, ECSE 302 and MATH 304.

For summer 2021, this course will be offered remote-synchronous. For more information, please reach out to the instructor. 

Dates: May 21 - June 11, 2021

Session: May Session

Time: MTWRF 9:35-12:35

Instructor: Shuai Xu

Credits: 3 credits

Department: Computer and Data Sciences

CSDS 440: Machine Learning

Machine learning is a subfield of Artificial Intelligence that is concerned with the design and analysis of algorithms that “learn” and improve with experience, While the broad aim behind research in this area is to build systems that can simulate or even improve on certain aspects of human intelligence, algorithms developed in this area have become very useful in analyzing and predicting the behavior of complex systems. Machine learning algorithms have been used to guide diagnostic systems in medicine, recommend interesting products to customers in e-commerce, play games at human championship levels, and solve many other very complex problems. This course is focused on algorithms for machine learning: their design, analysis and implementation. We will study different learning settings, including supervised, semi-supervised and unsupervised learning. We will study different ways of representing the learning problem, using propositional, multiple-instance and relational representations. We will study the different algorithms that have been developed for these settings, such as decision trees, neural networks, support vector machines, k-means, harmonic functions and Bayesian methods. We will learn about the theoretical tradeoffs in the design of these algorithms, and how to evaluate their behavior in practice. At the end of the course, you should be able to:
–Recognize situations where machine learning algorithms are applicable;
–Understand, represent and formulate the learning problem;
–Apply the appropriate algorithm(s), or if necessary, design your own, with an understanding of the tradeoffs involved;
–Correctly evaluate the behavior of the algorithm when solving the problem.

Prereq: EECS 391 or EECS 491

 

Please reach out to the instructor of this course to confirm modality, or check the course list in SIS

Dates: June 1 - July 27, 2021

Session: 8 Week Session

Time: MW 10:00-12:15

Instructor: Soumya Ray

Credits: 3 credits

Departments: Computer and Data Sciences, New 2021 Summer

ECSE 233: Introduction to Data Structures

Different representations of data: lists, stacks and queues, trees, graphs, and files. Manipulation of data: searching and sorting, hashing, recursion and higher order functions. Abstract data types, templating, and the separation of interface and implementation. Introduction to asymptotic analysis. The Java language is used to illustrate the concepts and as an implementation vehicle throughout the course.
Offered as CSDS 233 and ECSE 233.

Please reach out to the instructor of this course to confirm modality, or check the course list in SIS

Dates: June 1 - July 27, 2021

Session: 8 Week Session

Time: MWF 9:30-12:25

Instructor: Erman Ayday

Credits: 4 credits

Departments: Computer and Data Sciences, New 2021 Summer

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