|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
Three credits: Three lectures per week.Prerequisite: Algorithmics/ CSCI-501This course is a graduate level introduction to formal languages and the theoretical aspects of computing. It covers regular and context-free languages, as well as a hierarchy of formal languages and automata, finite and pushdown automata, the Turing machine, computabilty, decidability, and computational complexity.
Prerequisite:
CSCI 501
-
3.00 Credits
Three credits: Three lectures per week.Prerequisite: Operating Systems/ INFO 350 or equivalent undergraduate Operating Systems course.The covered topics will include:1. Distributed consensus algorithms-synchronization, leader election, consistency, atomicity2. Operating Systems structures-group communication, remote procedure calls, process migration, fault tolerance, distributed shared memory.3. Current Research-Mobile systems, Ad-Hoc Networks, Automatic computing, Large-scale computing, Parallel/cluster computing.
-
3.00 Credits
Three credits: Three lecture per week.Prerequisite: Consent of instructor.This is an advanced introductory course in networking with a focus of general purpose networking protocols such as the Internet Protocol (IP) and the Transmission Control Protocol (TCP). This course will also look at other networking infrastructure elements such as routing protocols and switching techniques. Both intra-domain and inter-domain routing will be discussed. Finally, this course will discuss some security and privacy concerns related to computer networking.
Prerequisite:
CSCI 360 OR INFO 355
-
3.00 Credits
Three credits: Three lectures per week.Prerequisite: Mathematical sophistication expected of a graduate student (Calculus, Discrete Math, Linear Algebra, Probability). Ability to program in a high level language (C, C++, or Java). Consent of the instructor.The coverd topics will include:1. Information Theory, Entropy, Information Gain.2. Sampling, Random Walks, Markov Chains.3. Probability and Statistics, Hypothesis Testing, Correlation, Coupling, Data Analysis, Bayesian Methods.4. Feature Sapce, Dimensionality Reduction, Data Comperession, Vector Quantization.5. Supervised Learning, Unsupervised Learning, Clustering, Decision Trees.6. Evolutionary Algorithms.7. Applications: Video, Imagery, Text, Bioinformatics, Medical Informatics.
Prerequisite:
CSCI 510
-
3.00 Credits
Three credits: Three lectures per week.Prerequisite: 35-553 Artificial Intelligence This course is designed to provide students with solid introduction to the theoretical and practical aspects of machine learning.
Prerequisite:
CSCI 530
-
3.00 Credits
Study of techniques for analysis and visualization of massive amounts of data. Includes hands-on experience in developing and using data mining software.Three credits: Three lectures per week.Prerequisite: Consent of the instructor.
Prerequisite:
CSCI 540
-
3.00 Credits
Pattern recognition techniques are used to classify phenomena that appear within multi-dimensional data into known or possibly unknown categories. Pattern recognition techniques have enjoyed commercial success in diverse applications such as classification of documents, voice recognition, and automated assembly of factory parts. Designing a pattern recognition system consists of a handful of major components: (1) sensing, (2) feature extraction and selection, (3) model building, (4) decision making, and (5) system performance evaluation. This class will provide an introductory graduate level survey of pattern recognition through lectures, readings from textbooks, readings from the research literature, and programming projects. Graduate level course, 3 credits.
Prerequisite:
CSCI 540
-
3.00 Credits
Three credits: Three lectures per week.Prerequisite: CSCI-540 Machine LearningOverview of analysisi and design principles for computer vision. Topics include camera geometry, motion detection, tracking, high level vision models.
Prerequisite:
CSCI 540
-
1.00 Credits
One credit: One lecture per week.Prerequisite: Graduate student standingThis course is needed to expose graduate students to a number of research areas across the discipline of Computer Science.
-
1.00 Credits
One credit: One lecture per week.Prerequisite: Graduate student standingThis seminar will train students in research problem formulation, experimental design, and empirical methods in Computer Science.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|