Web.cs.ucla.edu

Chapter 2: Entity-Relationship Model - CS | Computer Science

The discriminator of course-offering would be semester (including year) and section-number (if there is more than one section) If we model course-offering as a strong entity we would model course-number as an attribute. Then the relationship with course would be implicit in the course-number attribute

Actived: Friday Apr 16, 2021

Detail: http://web.cs.ucla.edu/classes/fall09/cs143/notes/er-handout

CS 145: Introduction to Data Mining - CS | Computer Science

Posted: (0 seconds ago) Description: This course introduces basic concepts, algorithms, and techniques of data mining on different types of datasets, including (1) vector data, (2) set data, (3) sequence data, and (4) text data. The class project involves hands-on practice of mining useful knowledge from large data sets. The course is an undergraduate-level computer science course. Also, the course may attract students from other disciplines who need to understand, develop, and use data mining techniques to analyze large amount…

Course Course Detail

Teaching · Tony Nowatzki - CS

Posted: (0 seconds ago) This course is designed to demystify computer systems, covering the basics of computer architecture, computer organization, operating systems and concurrency. First we build from the bottom up with detailed explanations of number systems, how they are employed in the instruction set architecture.

Course Course Detail

UCLA Computer Science 33 Syllabus

Posted: (0 seconds ago) Course Description CS 33, is a 4-unit course with four hours of lecture and two hours of discussion per week. Topics covered include: information representation and manipulation, floating-point representation, machine level representation of programs, code generation, code …

Course Course Detail

CS118: Computer Network Fundamentals

Posted: (0 seconds ago) The course will begin with an overview of networking concepts (Chapter 1) and then cover Network Application Protocols (Chapter 2) such as web (http) and email (smtp). Of particular interest in Chapter 2 is the Domain Name System (DNS) and the DNS will be the focus of a course project.

Course Course Detail

UCLA Computer Science M51A Syllabus

Posted: (0 seconds ago) CS m51A, same as Electrical Engineering m16, is a 4-unit course with four hours of lecture and two hours of discussion per week. Topics covered include: introduction to digital systems, specification and implementation of combinational and sequential systems, standard logic modules and programmable logic arrays, specification and implementation of algorithmic systems: data and control sections, number systems and arithmetic algorithms.

Course Course Detail

CS 111 Course Reference

Posted: (0 seconds ago) Most of the readings for this course will come from Remzi Arpaci-Dusseau 's Operating Systems in Three Easy Pieces.This is an online textbook, so you will not need to purchase a book for this class. However, the course covers certain topics that are not discussed in this textbook.

Course Course Detail

CS 239: Big Data Systems - CS | Computer Science

Posted: (0 seconds ago) Course Overview Modern computing has entered the era of big data. This class will introduce the concepts and state-of-the-art in modern big data systems. Specifically, we will cover these topics: _ Key challenges in big data processing _ Storage systems: HDFS, GFS, Big …

Course Course Detail

Computer Science 143 - CS

Posted: (0 seconds ago) Course Description . The goal of CS143 is to introduce students to relational database systems (RDB) and teach them how they work and how to use them for applications. Through the class, students will learn the RDB model and the SQL language. SQL is the standard language for the creation, query and modification of relational databases.

Course Course Detail

CM146: Introduction to Machine Learning (Winter 2019)

Posted: (0 seconds ago) Course description. Machine Learning encompasses the study of algorithms that learn from data. It has been a key component in a number of problem domains including computer vision, natural language processing, computational biology and robotics.

Course Course Detail

UCLA Computer Science M51A Syllabus

Posted: (0 seconds ago) CS m51A, same as Electrical Engineering m16, is a 4-unit course with four hours of lecture and two hours of discussion per week. Topics covered include: introduction to digital systems, specification and implementation of combinational and sequential systems, standard logic modules and programmable logic arrays, specification and implementation ...

Course Course Detail

Undergrad Student Enrollment in Undergraduate Computer ...

Posted: (0 seconds ago) If the course you are interested in is CS 32 (in a quarter in which it is offered), then instead of using the Enrollment Consideration Request Form, use the CS 32 form. Don't waste a first pass enrollment opportunity on CS 32 (unless it's for Winter quarter and you really want the 10:00 section). Requisites for CS courses are enforced.

Course Course Detail

CS260 ML Algorithms - University of California, Los Angeles

Posted: (0 seconds ago) The course will consist of biweekly lectures, problem sets that contain both mathemetical and MATLAB/Octave programming exercises, and two in-class exams. Prerequisites. Undergraduate level training or coursework in algorithms, linear algebra, calculus and multivariate calculus, basic probability and statistics; an undergraduate level course in ...

Course Course Detail

Network Verification and the Creative Habit

Posted: (0 seconds ago) In this reading course, we will divide the world into 3 parts. Recognizing that networks have a data plane (e.g., IP forwarding) and a control plane (that builds the forwarding plane, e.g., BGP) we will study key papers in: data plane verification (Anteater, Veriflow, HSA, NetPlumber, Atomic Predicates, NoD, and the use of symmetries for ...

Course Course Detail

EE 461L Software Design and Engineering Laboratory, Dr ...

Posted: (0 seconds ago) Course Organization As a four credit laboratory course, the class time will be organized around three hours of lecture per week, used to introduce the software engineering techniques and tools central to the course. An additional three hours of laboratory time per week will …

Software Engineering Course Detail

Syllabus - CS118: Computer Network Fundamentals - Fall ...

Posted: (0 seconds ago) Basic Course Information This course provides an introduction to fundamental concepts in the design and implementation of computer communication networks, their protocols, and applications. Topics to be covered include: layered network architecture, physical layer and data link protocols, network and transport protocols, unicast and multicast ...

Course Course Detail

CompSci 142 / CSE 142 Spring 2014, Course Reference

Posted: (0 seconds ago) Your course grade will be determined from the weighted combination of your scores on each of six projects, one Midterm, and one Final Exam. The weights of each of these are: Six projects, 40% ; Midterm, 30%; Final Exam, 30%; Determining final grades. Course grades will be curved. My grading policy is as follows:

Course Course Detail

Computer Science 143 - CS

Posted: (0 seconds ago) Course Description . The goal of CS143 is to introduce students to relational database systems (RDB) and teach them how to use them for applications. Through the class, students will learn the RDB model and the SQL language. SQL is the standard language for the creation, query and modification of relational databases.

Course Course Detail

Yizhou Sun (孙怡舟) - University of California, Los Angeles

Posted: (0 seconds ago) About Me [ CV]. I am currently an associate professor at Computer Science, UCLA. Prior to that, I joined Northeastern University as an assistant professor in 2013.

Course Course Detail

CS 145: Introduction to Data Mining - CS | Computer Science

Posted: (0 seconds ago) Class Schedule. CS 145: Introduction to Data Mining News [10/2/2017] First day of class. [10/1/2017] Book refers to: Jiawei Han, Micheline Kamber, and Jian …

Course Course Detail

CM146: Introduction to Machine Learning (Fall 2017)

Posted: (0 seconds ago) Course description. Machine Learning encompasses the study of algorithms that learn from data. It has been a key component in a number of problem domains including computer vision, natural language processing, computational biology and robotics.

Course Course Detail

CS111 Syllabus - University of California, Los Angeles

Posted: (0 seconds ago) This course introduces an extremely wide range of new concepts, and so involves a great deal of reading. Most of the readings for this course will come from Remzi Arpaci-Dusseau's Operating Systems in Three Easy Pieces. This text was selected, after evaluating numerous alternatives, for several reasons:

Course Course Detail

CS 111 Lab Manual

Posted: (0 seconds ago) Projects follow quickly after the readings and lectures in which the associated principles are presented. Project deliverables are spread (relatively) uniformly throughout the course (one per week). This is done to keep you from getting in trouble when you discover that you cannot complete a …

Course Course Detail

Cho-Jui Hsieh

Posted: (0 seconds ago) Machine Learning. CS260, Winter 2019. Course Page

Course Course Detail

CS188: Introduction to Machine Learning (Winter 2017)

Posted: (0 seconds ago) Course description. Machine Learning encompasses the study of algorithms that learn from data. It has been a key component in a number of problem domains including computer vision, natural language processing, computational biology and robotics.

Course Course Detail

CS M226 / BIOINF M226/ HUMGEN M226: Machine Learning for ...

Posted: (0 seconds ago) Course format. Homework (50%): There will be periodic homeworks. Questions on the homework will include programming exercises and data analyses. We will use gradescope to manage submission of homeworks. Homeworks are due at 11:59pm on the due date. Late submissions will not be accepted

Course Course Detail

cs161 Syllabus - web.cs.ucla.edu

Posted: (0 seconds ago) This is an undergraduate course that introduces the fundamental problem solving and knowledge representation paradigms of artificial intelligence. The first couple lectures review the LISP programming language. The next part of the course will cover problem solving including problem spaces, brute-force and heuristic search, two-player games ...

Course Course Detail

Syllabus for UCLA Computer Science 97

Posted: (0 seconds ago) A major part of this course is a collaborative software project, in which student groups design and construct a user-facing application involving multiple software components that communicate across a network. Lectures Files, editing, and shells. Multiuser and multiprocess operating systems; CLI basics (e.g., Bash) Unix file system organization

Course Course Detail

CS M226 / BIOINF M226/ HUMGEN M226: Machine Learning for ...

Posted: (0 seconds ago) Course format. Homework: There will be five homeworks. Questions on the homework will include programming exercises and data analyses. You can are strongly encouraged to use R (R is free software. See here for details ). The homeworks must be submitted in hard copy in class on the day they are due. Late submissions will not be accepted.

Course Course Detail

COM SCIM229S-2 / BIOL CHM229S-2 / HUM GENM229S-2: …

Posted: (0 seconds ago) The course aims to introduce CS/Statistics students to an important set of problems and Bioinformatics/Human Genetics students to a rich set of tools. Prerequisites. Familiarity with probability, statistics, linear algebra and algorithms is expected. No familiarity with biology is needed.

Course Course Detail

Guy Van den Broeck - CS 161 - Fundamentals of Artificial ...

Posted: (0 seconds ago) Course Description. This course studies the design of intelligent agents. It introduces the fundamental problem-solving and knowledge-representation paradigms of artificial intelligence. We will study the AI programming language LISP, state-space and problem reduction methods, brute-force and heuristic search, planning techniques, two-player ...

Course Course Detail

Winter 2021 CS 32

Posted: (0 seconds ago) The Winter 2021 CS 32 website is no longer accessible. Academic Integrity. Project 4 spec Test cases for Project 4 Test cases for Project 4

Course Course Detail

CS 249: Special Topics - CS | Computer Science

Posted: (0 seconds ago) About the Course. This is a graduate-level research-oriented course offered in Winter 2021. The course aims to introduce and discuss recent advances in graph neural networks (GNNs), with the goal to design deep learning algorithms for graph data for different graph applications.

Course Course Detail

Course COM SCI M282A: Foundations of Cryptography

Posted: (0 seconds ago) CS and Math Departments Instructor: Rafail Ostrovsky, Office: 3732D Boelter Hall. Office hours: By appointment or after class. Lectures: M,W 2-3:50pm. Description: This is a graduate course that introduces students to the theory of cryptography, stressing rigorous definitions and proofs of security. Topics include notions of hardness, one-way functions, hard-core bits, pseudo-random generators ...

Course Course Detail

Guy Van den Broeck - CS 161 - Fundamentals of Artificial ...

Posted: (0 seconds ago) This course studies the design of intelligent agents. It introduces the fundamental problem-solving and knowledge-representation paradigms of artificial intelligence. We will study the AI programming language LISP, state-space and problem reduction methods, brute-force and heuristic search, planning techniques, two-player games, and recent ...

Course Course Detail

Lecture 1 - CS

Posted: (0 seconds ago) Labs: 4 labs which may be with a partner Design Problem: An extension to a lab. May be with a partner; Minilabs: Solo labs. Scribe Notes: Lecture notes. OS Paper: A 2-3 paged paper on a recent topic in operating systems Midterm: 100 minutes in class. Open note. Final: 180 minutes. Open note. Late Policy . Assignments are due 23:55.

Course Course Detail

CS6220: Data Mining Techniques - University of California ...

Posted: (0 seconds ago) The course is a graduate-level computer science course, which is also a good option for senior-level computer science undergraduate students interested in the field. Also, the course may attract students from other disciplines who need to understand, develop, and use data mining systems to analyze large amounts of data.

Course Course Detail

Miryung Kim: Teaching - CS

Posted: (0 seconds ago) This course will introduce students to the foundations, techniques, tools, and applications of automated software engineering technology. Students will develop, extend, and evaluate a mini automated software engineering analysis tool and assess how the tool fits into the software development process. This class is intended to students to ...

Software Engineering Course Detail

Winter 2004 CS 31 (Shinnerl) Course Syllabus

Posted: (0 seconds ago) The official software platform for this course is Microsoft Visual Studio .NET on Microsoft Windows XP. If you wish to install the course software on your own computer, you may pick up your free copies of the installation CD's from Julie Austin at BH 2567, Mon--Fri 9am--4:30PM except 12--1PM lunch. Required Text: Absolute C++ by W. Savitch.

Course Course Detail

CompSci 142 / CSE 142 Spring 2014, Lab Manual

Posted: (0 seconds ago) Submitting your assignments. When you complete each project, you must submit it to us electronically. For each project, you will find an assignment dropbox in the EEE course website. Please name your submission last_first_projectx.zip, where last and first are your last and first name, and x …

Course Course Detail

CM229: Advanced Computational Genetics (Spring 2019)

Posted: (0 seconds ago) Course description. This is a graduate-level seminar course focusing on current topics in computational genetics. It is intended for PhD students with interest in research problems in computational genetics. Topics will cover problems in medical and population genetics, statistical models that are needed to make inferences in these problems ...

Course Course Detail

Syllabus for UCLA Computer Science 33, Fall 2018

Posted: (0 seconds ago) The following textbook chapters are useful and entertaining but are not part of this course: §4, §11. All assignments are due at 23:55 on the date specified. Final exam. The final exam is three hours and will be held at the time scheduled by the registrar.

Course Course Detail