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M tech thesis in computer science
Legal informatics based on representation of regulations in computable form. Encoding regulations facilitate creation of legal information systems with significant practical value. Convergence of technological trends, growth of the Internet, advent of semantic web technology, and progress in computational logic make computational law prospects better. Topics: current state of computational law, prospects and problems, philosophical and legal implications. This course is *Cross* listed with LAW 4019. Prerequisite: basic concepts of programming.
Principles and practice of engineering of computer software and hardware systems. Topics include: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; security, and encryption; and performance optimizations. Prerequisite: 107.
M tech thesis in computer science students
The Department of Computer Science at Montclair State University supports the Bachelor of Science degrees in Computer Science, Information Technology and Science Informatics. It also offers a Master of Science degree in Computer Science and a MS in Computer Science with an Applied Information Technology and Information Technology Concentrations.
We offer other students opportunities to enhance their education through an undergraduate minor in Computer Science and a combined Computer Science BS/MS program, a CISCO Networking Academy, and for graduate students only, the Certificate in Object Oriented Computing and a sequence of graduate level computing courses for those without a background in computing.
The tens digit indicates the area of Computer Science it addresses:
Capstone Biomedical Informatics (BMI) experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.
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Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into "big data" disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances. The course will start with an introduction to deep learning and overview the relevant background in genomics and high-throughput biotechnology, focusing on the available data and their relevance. It will then cover the ongoing developments in deep learning (supervised, unsupervised and generative models) with the focus on the applications of these methods to biomedical data, which are beginning to produced dramatic results. In addition to predictive modeling, the course emphasizes how to visualize and extract interpretable, biological insights from such models. Recent papers from the literature will be presented and discussed. Students will be introduced to and work with popular deep learning software frameworks. Students will work in groups on a final class project using real world datasets. Prerequisites: College calculus, linear algebra, basic probability and statistics such as CS109, and basic machine learning such as CS229. No prior knowledge of genomics is necessary.
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CS 109, Introduction to Probability for Computer Scientists
Computer Science and Engineering Association (2016-17)
Programs leading to a Bachelor's degree in computer science are offered by the undergraduate colleges at Rutgers.
CS 198, Teaching Computer Science
A recent study found that computer science majors earn the highest median based salary amongst all college majors.
CS 208E, Great Ideas in Computer Science
Head of the DepartmentDepartment of Computer Science and EngineeringTKM College of EngineeringKollam
Computer Science and Engineering Association (2015-16)
KERNL, The Newletter of Department of Computer Science and Engineering is providing a platform to present their technical ideas and innovative thoughts.
Computer Science and Engineering
The uniqueness of DIAT is that fresh students have an opportunity to interact with service officers who are doing their M. Tech & Share their in field experience with them. The students have an opportunity to visit DRDO laboratories & get opportunity to listen and interaction with scientist engaged in various disciplines carrying out state of art, research & development
CS 54N. Great Ideas in Computer Science. 3 Units.
For a minor in Computer Science, a candidate must complete 20 units of Computer Science coursework numbered 200 or above, except for the 100-level courses listed on the (pdf). At least three of the courses must be master’s core courses to provide breadth and one course numbered 300 or above to provide depth. One of the courses taken must include a significant programming project to demonstrate programming efficiency. Courses must be taken for a letter grade and passed with a grade of 'B' or better. Applications for a minor in Computer Science are submitted at the same time as admission to candidacy.
Material Science and Chemical Technology
Recent advances in computing may place us at the threshold of a unique turning point in human history. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. The prospect of "turning over the keys" to increasingly autonomous systems raises many complex and troubling questions. How will society respond as versatile robots and machine-learning systems displace an ever-expanding spectrum of blue- and white-collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend? What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life? The goal of CS22 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines.
Materials Science and Technology (MST)
Technical developments in artificial intelligence (AI) have opened up new opportunities for entrepreneurship, as well as raised profound longer term questions about how human societal and economic systems may be reorganized to accommodate the rise of intelligent machines. In this course, closely cotaught by a Stanford professor and a leading Silicon Valley venture capitalist, we will examine the current state of the art capabilities of existing artificial intelligence systems, as well as economic challenges and opportunities in early stage startups and large companies that could leverage AI. We will focus on gaps between business needs and current technical capabilities to identify high impact directions for the development of future AI technology. Simultaneously, we will explore the longer term societal impact of AI driven by inexorable trends in technology and entrepreneurship. The course includes guest lectures from leading technologists and entrepreneurs who employ AI in a variety of fields, including healthcare, education, selfdriving cars, computer security, natural language interfaces, computer vision systems, and hardware acceleration.
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