printer friendly pageCS 246: Mining Massive Data Sets
The course will discuss data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis will be on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Topics include: Frequent itemsets and Association rules, Near Neighbor Search in High Dimensional Data, Locality Sensitive Hashing (LSH), Dimensionality reduction, Recommender Systems, Clustering, Link Analysis, Large-scale machine learning, Data streams, Analysis of Social-network Graphs, and Web Advertising. Prerequisites: At lease one of CS107 or
CS145; At least one of CS109 or STAT116, or equivalent.
Terms: Win
| Units: 3-4
Instructors:
Leskovec, J. (PI)
;
Ullman, J. (PI)
CS 246H: Mining Massive Data Sets Hadoop Lab
Supplement to
CS 246 providing additional material on Hadoop. Students will learn how to implement data mining algorithms using Hadoop, how to implement and debug complex MapReduce jobs in Hadoop, and how to use some of the tools in the Hadoop ecosystem for data mining and machine learning. Topics: Hadoop, MapReduce, HDFS, combiners, secondary sort, distributed cache, SQL on Hadoop, Hive, Cloudera ML/Oryx, Mahout, Hadoop streaming, implementing Hadoop jobs, debugging Hadoop jobs, TF-IDF, Pig, Sqoop, Oozie, HBase, Impala. Prerequisite:
CS 107 or equivalent.
Terms: Win
| Units: 1
Instructors:
Templeton, D. (PI)
CS 247: Human-Computer Interaction Design Studio
Project-based focus on interaction design process, especially early-stage design and rapid prototyping. Methods used in interaction design including needs analysis, user observation, sketching, concept generation, scenario building, and evaluation. Prerequisites: 147 or equivalent background in design thinking; 106B or equivalent background in programming. Recommended:
CS 142 or equivalent background in web programming. Enrollment limited to 40 students based on an application to be given out the first day of class.
Terms: Win, Spr
| Units: 3-4
Instructors:
Agrawala, M. (PI)
;
Bernstein, M. (PI)
;
Chilton, L. (PI)
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Instructors:
Agrawala, M. (PI)
;
Bernstein, M. (PI)
;
Chilton, L. (PI)
;
Kaye, J. (SI)
;
Roeber, H. (SI)
;
Stanford, J. (SI)
;
Tang, J. (SI)
CS 248: Interactive Computer Graphics
This is the second course in the computer graphics sequence, and as such it assumes a strong familiarity with rendering and image creation. The course has a strong focus on computational geometry, animation, and simulation. Topics include splines, implicit surfaces, geometric modeling, collision detection, animation curves, particle systems and crowds, character animation, articulation, skinning, motion capture and editing, rigid and deformable bodies, and fluid simulation. As a final project, students implement an interactive video game utilizing various concepts covered in the class. Games may be designed on mobile devices, in a client/server/browser environment, or on a standard personal computer. Prerequisite:
CS148.
Terms: Win
| Units: 3-4
Instructors:
Fedkiw, R. (PI)
CS 255: Introduction to Cryptography
For advanced undergraduates and graduate students. Theory and practice of cryptographic techniques used in computer security. Topics: encryption (symmetric and public key), digital signatures, data integrity, authentication, key management, PKI, zero-knowledge protocols, and real-world applications. Prerequisite: basic probability theory.
Terms: Win
| Units: 3
Instructors:
Boneh, D. (PI)
CS 257: Logic and Artificial Intelligence (PHIL 356C)
This is a course at the intersection of philosophical logic and artificial intelligence. The goal of the course is to understand the role that expressive logical frameworks might play in AI, and to gain a deeper understanding of how different logical systems relate, and what features of a logical system could make it useful for representation and/or reasoning. Specific themes may include: 1. Tradeoff between complexity and expressivity 2. Capturing subtle reasoning about agent mental states 3. Defeasibility, causality, and the relation between logic and probability 4. Logical formalizations of legal and normative reasoning 5. Combining statistical learning and inference with rich logical structure 6. Logical systems close to the structure of natural language ("natural logics"). Prerequisites: It is expected that students already have a solid background in logic.
Phil 151 is ideal, but
Phil 150 or
CS 157 would be acceptable, with the understanding that there may be some catching up to do. 2 unit option for PhD students only.
Terms: Win
| Units: 2-4
Instructors:
Icard, T. (PI)
CS 261: Optimization and Algorithmic Paradigms
Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to online algorithms. Prerequisite: 161 or equivalent.
Terms: Win
| Units: 3
Instructors:
Goel, A. (PI)
;
Shen, C. (TA)
CS 264: Beyond Worst-Case Analysis
This course is motivated by problems for which the traditional worst-case analysis of algorithms fails to differentiate meaningfully between different solutions, or recommends an intuitively "wrong" solution over the "right" one. This course studies systematically alternatives to traditional worst-case analysis that nevertheless enable rigorous and robust guarantees on the performance of an algorithm. Topics include: instance optimality; smoothed analysis; parameterized analysis and condition numbers; models of data (pseudorandomness, locality, diffuse adversaries, etc.); average-case analysis; robust distributional analysis; resource augmentation; planted and semi-random graph models. Motivating problems will be drawn from online algorithms, online learning, constraint satisfaction problems, graph partitioning, scheduling, linear programming, hashing, machine learning, and auction theory. Prerequisites:
CS161 (required). CS261 is recommended but not required.
Terms: Win
| Units: 3
Instructors:
Roughgarden, T. (PI)
CS 269G: Almost Linear Time Graph Algorithms (MS&E 313)
Over the past decade there has been an explosion in activity in designing new provably efficient fast graph algorithms. Leveraging techniques from disparate areas of computer science and optimization researchers have made great strides on improving upon the best known running times for fundamental optimization problems on graphs, in many cases breaking long-standing barriers to efficient algorithm design. In this course we will survey these results and cover the key algorithmic tools they leverage to achieve these breakthroughs. Possible topics include but are not limited to, spectral graph theory, sparsification, oblivious routing, local partitioning, Laplacian system solving, and maximum flow. Prerequisites: calculus and linear algebra.
Terms: Win
| Units: 3
Instructors:
Sidford, A. (PI)
CS 270: Modeling Biomedical Systems: Ontology, Terminology, Problem Solving (BIOMEDIN 210)
Methods for modeling biomedical systems and for building model-based software systems. Emphasis is on intelligent systems for decision support and Semantic Web applications. Topics: knowledge representation, controlled terminologies, ontologies, reusable problem solvers, and knowledge acquisition. Students learn about current trends in the development of advanced biomedical software systems and acquire hands-on experience with several systems and tools. Prerequisites:
CS106A, basic familiarity with biology.
Terms: Win
| Units: 3
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