[Cs-affiliates] CS 591 Topics courses - Spring 2017
cs at bu.edu
Fri Oct 28 11:43:46 EDT 2016
Dear CS students,
As many of you know, registration opens for 3rd and 4th year students on Sunday. Below you will find expanded descriptions of the Computer Science Topics courses (those listed as CS 591) to be offered in Spring 2017. Please review the descriptions, and if you have further questions feel free to reach out to the instructors.
A couple of important reminders:
· You should register for 4.0 credits when registering for these courses. You may see that they are listed as variable credit. All CS591 courses are 4.0 credit courses.
· You can register for more than one CS 591 class, so long as they are different sections and you are not repeating a section you have already taken.
Regarding courses that are already full, closed, or will be full by the time you register. If you are blocked from registering for a course please email cs at bu.edu<mailto:cs at bu.edu> to get on a course wait list. As many of you know from experience, the wait lists are crucial in making sure CS students are given priority as seats become available. But they also let us know how much unmet demand there is for our courses so we can adjust and plan accordingly.
As always, any questions or concerns please don’t hesitate to reach out.
111 Cummington Mall
Boston, MA 02215
CAS CS 591 E1
Mobile Application Development
Course Description: Students will utilize agile software engineering practices in this hands-on course to design and implement mobile applications using Java and the Android SDK. Students will initially implement several small mobile applications utilizing core android technologies, after which students will be grouped into small groups, collaborating on a larger final project. Topics will include UI development, action bars, multi-touch, gestures, database and file I/O. Students will also learn to make rich applications by consuming location and sensor information from device hardware. No previous mobile application development experience is required, but a strong understanding of object-oriented programming and database development (from CS 112 and CS 460 or equivalent) is necessary.
Instructor: Shereif El-Sheikh
*As of the writing of this email the course is still not added to the University Class Schedule, but should be in the coming hours. Please check the University Class Schedule regularly if it’s not added by Sunday. The tentative offering of this course will be Tuesdays, 5:00 – 8:00PM.
CAS CS 591 L1
Data Mechanics for Pervasive Systems and Urban Applications
Course Description: This course is about how data can move through institutions and computational infrastructures to inform decisions and operations (often in real time) within large systems such as cities that contain a variety of widely distributed, embedded sensors and devices (e.g., sensor networks and smart power grids). The course covers and teaches students to apply some of the tools and methods that computer science and computational thinking provide for facilitating data collection, retrieval, integration, and interpretation in application areas like urban informatics and distributed systems. Students will learn how to use the relational and MapReduce paradigms to assemble analysis, optimization, and decision-making algorithms that can scale up to handle large amounts of static or streaming data. Formal techniques for modeling and ensuring predictable, reliable, and correct behavior of such algorithms, as well as techniques for tracking data provenance, will also be presented. Other potential topics include crowdsourcing, socio-adaptive systems, and modern tools for building offline and online visualizations. Students will apply the tools and methods presented to build platforms and applications that work with real data sets dealing with aspects of urban environments such as mobility (e.g., walkability), employment, traffic and parking, emissions, energy consumption, public safety, and others.
Instructor: Andrei Lapets
CAS CS 591 S1
Course Description: This course will present a fundamentals of digital audio processing from a computer science point of view. Since our computer science curriculum includes no required courses in physics or in signal processing ( e.g., ENG EC 401 Signals and Systems, ENG EC 416 Digital Signal Processing), we shall develop the subject from first principles, emphasizing programming approaches to the analysis of sound, particular music and voice. Classes will typically be divided between theory and practice, with an emphasis on the programming techniques necessary to solve various problems. Weekly programming assignments will be in Python, and you will do a final project on a subject of your choosing.
Instructor: Wayne Snyder
CAS CS 591 S2
Course Description: This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. For example, asked to recognize faces, a deep neural network may learn to represent image pixels first with edges, followed by larger shapes, then parts of the face like eyes and ears, and, finally, individual face identities. Deep learning is behind many recent advances in AI, including Siri’s speech recognition, Facebook’s tag suggestions and self-driving cars.
We will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains like speech recognition and computer vision. Prerequisites: a strong mathematical background in calculus, linear algebra, and probability & statistics (students will be required to pass a math prerequisites test), as well as programming in Python and C/C++. There will be assignments and a final project.
Instructors: Kate Saenko and Brian Kulis
CAS CS 506 A1
Computational Tools for Data Science (previously listed as
CAS CS 591 T1)
Course Description: Covers practical skills in working with data and introduces a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. Emphasizes hands-on application of methods via programming. Prereqs: CS 108 or CS 111; CS 132 or MA 242 or MA 442; CS 112 Recommended
Instructor: Evimaria Terzi
*This course has been permanently added to our course inventory as CAS CS 506. Previously offered as CS 591 Data Science with Python
CAS CS 591 T2
Data Analytics: Theory and Applications
Course Description: This course is designed to give graduate students a thorough grounding in the theory, and algorithms needed to do research and applications in data analytics. Topics that we will cover include nearest-neighbor search in high dimensions (LSH), data mining (dimensionality reduction, mining data streams, collaborative filtering and recommendation systems), mining large networks (clustering, dense subgraphs, balanced partitioning), and machine learning methods for classification, regression, semi-supervised learning. We will draw techniques from both combinatorial, and convex optimization, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.
Each student will be required to scribe for one lecture (10%), complete an ambitious project around a real-world problem (70%), and write a final exam (20%).
Class prerequisites: Linear-algebra, calculus, probability, programming, data structures and algorithms.
Instructors: Babis Tsourakakis
CAS CS 591 V1
Applied Cryptography: Design & Practice
Course Description: Introduces the techniques in the theory, design, and cryptanalysis of symmetric cryptography primitives. Examines several primitives including stream ciphers, block ciphers, and collision-resistant hash functions; specific ciphers studied in detail include DES, AES, and the SHA family of hash functions. Analyzes the mathematical strength of these primitives toward linear and differential cryptanalysis. Explores provably-secure constructions of symmetric encryption and signature schemes from these building blocks using, e.g., HMAC and modes of operation over a block cipher. Prerequisites: CASCS210. Familiarity with the concepts in CASCS235 and CASCS237, or consent of instructor. Recommended but not required: prior exposure to cryptography through CASCS538 or CASCS558.
Instructor: Mayank Varia
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