Introduction to Computing using Python
CS 1110
Introduces programming and problem-solving using Python. Emphasizes
principles of software development, style, and testing. Topics
include an operational model of Python execution, procedures and
functions, iteration, recursion, lists, strings, algorithms,
exceptions, object-oriented programming, and GUIs (graphical user
interfaces). Assignments use graphics and GUIs to help develop
fluency and understanding.
Object Oriented Programming and Data Structures
CS 2110
Intermediate programming in a high-level language and introduction
to computer science. Topics include program structure and
organization, object-oriented programming (classes, objects, types,
sub-typing), graphical user interfaces, algorithm analysis
(asymptotic complexity, big “O” notation), recursion, data
structures (lists, trees, stacks, queues, heaps, search trees, hash
tables, graphs), graph algorithms. Java is the principal programming
language.
Discrete Structures
CS 2800
Covers the mathematics that underlies most of computer science.
Topics include mathematical induction; logical proof; propositional
and predicate calculus; combinatorics and discrete mathematics; some
basic elements of basic probability theory; basic number theory;
sets, functions, and relations; graphs; and finite-state machines.
These topics are discussed in the context of applications to many
areas of computer science, such as the RSA cryptosystem and web
searching.
Functional Programming and Data Structures
CS 3110
Advanced programming course that emphasizes functional programming
techniques and data structures. Programming topics include recursive
and higher-order procedures, models of programming language evaluation
and compilation, type systems, and polymorphism. Data structures and
algorithms covered include graph algorithms, balanced trees,
memory heaps, and garbage collection. Also covers techniques for
analyzing program performance and correctness.
Computer Vision
CS 4670
An in-depth introduction to computer vision. The goal of computer vision
is to compute properties of our world-the 3D shape of an environment,
the motion of objects, the names of people or things-through analysis
of digital images or videos. The course covers a range of topics,
including 3D reconstruction, image segmentation, object recognition,
and vision algorithms fro the Internet, as well as key algorithmic,
optimization, and machine learning techniques, such as graph cuts,
non-linear least squares, and deep learning. This course emphasizes
hands-on experience with computer vision, and several large programming projects.
Computer System Organization and Programming
CS 3410
Introduction to computer organization, systems programming and the hardware/
software interface. Topics include instruction sets, computer arithmetic,
datapath design, data formats, addressing modes, memory hierarchies including
caches and virtual memory, I/O devices, bus-based I/O systems, and multicore
architectures. Students learn assembly language programming and design a
pipelined RISC processor.
Introduction to Database Systems
CS 4320
Introduction to modern database and data storage systems. Concepts
covered include data models, query languages, database designs, storage
structures, access methods, query processing and optimization,
transaction management, and recovery in both relational and nonrelation
data storage systems.
Artificial Intelligence
CS 4700
Challenging introduction to the major subareas and current research directions
in artificial intelligence. Topics include: knowledge representation, heuristic search,
problem solving, natural-language processing, game-playing, logic and deduction,
planning, and machine learning.
Practicum in Artificial Intelligence
CS 4701
Project portion of CS 4700. Topics include knowledge representation
systems, search procedures, game-playing, automated reasoning, concept
learning, reinforcement learning, neural nets, genetics algorithms,
planning, and truth maintenance.
Operating Systems
CS 4410
Introduction to the design of systems programs, with emphasis on multiprogrammed
operating systems. Topics include concurrency, synchronization, deadlocks,
memory management, protection, input-output methods, networking, file systems
and security. The impact of network and distributed computing environments on
operating systems is also discussed.
Introduction to Analysis of Algorithms
CS 4820
Develops techniques used in the design and analysis of algorithms,
with an emphasis on problems arising in computing applications.
Example applications are drawn from systems and networks, artificial intelligence,
computer vision, data mining, and computational biology.
This course covers four major algorithm design techniques
(greedy algorithms, divide-and-conquer, dynamic programming, and network flow),
undecidability and NP-completeness, and algorithmic techniques for intractable problems
(including identification of structured special cases , approximation algorithms,
local search heuristics, and online algorithms).
