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Forensics (parent submitted) – Easy Peasy All-in-One …
Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students are expected to produce an original research paper on a relevant topic. Prerequisites: / or .
This course will review some of the greatest discoveries in modern cryptography: zero-knowledge proofs, factoring algorithms, elliptic-curve cryptography, post-quantum cryptography, and more. Some of the topics we will cover have immediate practical applications. Other topics we will study for their potential future applications. And yet others we will study for the theoretical insights they provide. The course readings will be a combination of the original "classic" papers and more modern treatments of the same topics. Prerequisite: , an equivalent course, or permission of instructors.
6/1/2006 · Master Thesis in Computer Forensics
Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments. Prerequisite: or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Same as: , ,
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.
Same as: , ,
Research Topics - ForensicsWiki
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Master's Thesis: Disheveled Digital Forensics | LinkedIn
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.
Master's Thesis: Disheveled Digital Forensics
(cross-leveled with ). An introductory study of computation and formal languages by means of automata and related grammars. The theory and applications of finite automata, regular expressions, context free grammars, pushdown automata and Turing machines are examined. May not be counted toward Computer Science MS/PHD.
Credit Hours: 3
Prerequisites: C- or higher in
dissertation for computer forensics, ..
This is a crash course in how to use a stripped-down computer system aboutnthe size of a credit card (the rasberry pi computer) to control as manyndifferent sensors as we can implement in ten weeks, including LEDs, motionnsensors, light controllers, and accelerometers. The ability to fearlesslyngrab a set of hardware devices, examine the data sheet to see how to usenit, and stitch them together using simple code is a secret weapon thatnsoftware-only people lack, and allows you to build many interestingngadgets. We will start with a ``bare metal'' system --- no operatingnsystem, no support --- and teach you how to read device data sheetsndescribing sensors and write the minimal code needed to control themn(including how to debug when things go wrong, as they always do). Thisncourse differs from most in that it is deliberately mostly about what andnwhy rather than how --- our hope is that the things you are able at the endnwill inspire you to follow the rest of the CS curriculum to understandnbetter how things you've used work. Prerequisites: knowledge of the Cnprogramming language. A Linux or Mac laptop that you are comfortablencoding on.
Computer Forensic Essays: Examples, Topics, Titles, & …
(cross-leveled with ). An introductory study of computation and formal languages by means of automata and related grammars. The theory and applications of finite automata, regular expressions, context free grammars, pushdown automata and Turing machines are examined. May not be counted toward CS MS/PHD.
Credit Hours: 3
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