Cs 178 uci

Cs 178 uci

The diverse research interests of our faculty are reflected directly in our educational programs. Computer Science faculty teach most of the undergraduate and graduate courses for the degree programs in both Computer Science and Information and Computer Science. We jointly offer with our colleagues in The Henry Samueli School of Engineering an undergraduate degree in Computer Science and Engineering, as well as the graduate program in Networked Systems.

Our department collaborates with many other institutions in the United States and abroad, and its doors are always open to a multitude of visitors and collaborators from all corners of the globe.

Advanced programming language concepts for more complex, higher performance software design. Focuses on strengthening programming, debugging, and problem solving skills. Computer Science Engineering Majors have first consideration for enrollment. Digital Image Processing. Introduction to the fundamental concepts of digital signal and image processing as applicable in areas such as multimedia, graphics, AI, data mining, databases, vision, or video games.

Topics include image representation, space- and frequency-domain transformations, filters, segmentation, and compression. CSE 46 with a grade of C or better. Introduction to the fundamental principles of 3D computer graphics including polygonal modeling, geometric transformations, visibility algorithms, illumination models, texturing, and rasterization. Use of an independently-learned 3D graphics API to implement these techniques.

CSE 45C with a grade of C or better. Computer Game Development. Introduction to the principles of interactive 2D and 3D computer game development. Concepts in computer graphics, algorithms, software engineering, art and graphics, music and sound, story analysis, and artificial intelligence are presented and are the basis for student work. Projects in Advanced 3D Computer Graphics. Projects in advanced 3D graphics such as illumination, geometric modeling, visualization, and animation.

Topics include physically based and global illumination, solid modeling, curved surfaces, multiresolution modeling, image-based rendering, basic concepts of animation, and scientific visualization. Discrete event-driven simulation; continuous system simulation; basic probability as pertaining to input distributions and output analysis; stochastic and deterministic simulation; static and dynamic system simulation.

Computational Photography and Vision. Introduces the problems of computer vision through the application of computational photography. Specific topics include photo-editing image warping, compositing, hole fillingpanoramic image stitching, and face detection for digital photographs. Project in Computer Vision.

Department of Computer Science

Students undertake construction of a computer vision system. Topics include automatically building 3D models from photographs, searching photo collections, robot navigation, and human motion tracking. An introduction to information retrieval including indexing, retrieval, classifying, and clustering text and multimedia documents. Introduction to Data Management.Students in this project class will work in small teams to develop artificial intelligence and machine learning algorithms and apply them to problems of their design on the Minecraft video game platform.

The problems can include, for example, automated navigation for player agents, planning for gathering items for a recipe, computer vision for understanding the environment, natural language interface, and so on. We anticipate the range of projects to cover a large variety of concepts in machine learning and artificial intelligence such as state space search, planning, constraint satisfaction problems, reinforcement learning, supervised learning, unsupervised learning, and applied ML tasks such as NLP and computer vision.

Projects will also utilize the whole pipeline of practical software engineering, such as revision control, issue tracking, maintaining documentation, implementing advanced algorithms, reusing existing software libraries, and documentation, amongst others. Prerequisites At minimum: An undergraduate machine learning course CS or equivalent.

An undergraduate artificial intelligence course CS or equivalent. Programming assignments will require a working familiarity with Python, along with familiarity with data structures and algorithms. Contact me if you are concerned about your background for the course. The system is highly catered to getting you help fast and efficiently from classmates and myself.

Rather than emailing questions to me, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team piazza.

There will be no questions asked, you can use these days as you see fit. However, if you run out of grace days, and still submit late, your submission will not be graded and you will get a 0 for that submission and no excuse will be entertained.

Any leeway in this policy will only be entertain if pre-arranged before submission with the instructor, and under extenuating circumstances. Academic Honesty Academic honesty is a requirement for passing this class. Any student who compromises the academic integrity of this course is subject to a failing grade. The work you submit must be your own. Academic dishonesty includes, but is not limited to copying answers from another student, allowing another student to copy your answers, communicating exam answers to other students during an exam, attempting to use notes or other aids during an exam, or tampering with an exam after it has been corrected and then returning it for more credit.

It is your responsibility to read and understand these policies.

CS178 Project Demo

Note that any instance of academic dishonesty will be reported to the Academic Integrity Administrative Office for disciplinary action and may be cause for a failing grade in the course. DBH Office Hours. PiazzaCanvas.Also, a possibly helpful LaTeX template I use for homeworks and solutions. Or, this link has another nice way to include Matlab code in LaTeX. How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data?

These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

Machine learning as a field is now incredibly pervasive, with applications from the web search, advertisements, and suggestions to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

Most or all code should be Octave compatible, so you may use Octave if you prefer. There is no required textbook for the class. I use Piazza to manage student discussions and questions.

Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations e. If you use Octave, please be careful to use Matlab-compatible syntax not Octave extensionssince otherwise I or the TA may be unable to interpret your code. You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

Previous year's lectures ba, are also available. Last modified January 19,at PM. Classes Group Research Publications Code.Computer science is the catalyst for every evolutionary — and revolutionary — step in computer development. From mathematical theories, data structures and algorithms to the operating systems and programs that employ them, an understanding of computer science is essential if you wish to develop the next advances in computer technology and applications.

The Computer Science program at UC Irvine is internationally recognized for its unique group of faculty and researchers, outstanding students and cutting edge educational programs.

CS178: Machine Learning and Data Mining

This specialization focuses on fundamental computational techniques, including their analysis and applications to topics in computer vision, computer games, graphics, artificial intelligence, and information retrieval.

Topics include data structures, graph and network algorithms, computational geometry, probabilistic algorithms, complexity theory, and cryptography. Architecture and Embedded Systems. Students in this specialization will build upon a strong foundation in software and hardware and learn how to design networked embedded systems, and efficient computer architectures for a diverse set of application domains such as gaming, visualization, search, databases, transaction processing, data mining, and high-performance and scientific computing.

This specialization introduces students to the interdisciplinary intersection of biology and medicine with computer science and information technology. Students who complete the specialization will understand biomedical computing problems from the computer science perspectives, and be able to design and develop software that solves computational problems in biology and medicine.

General Computer Science. This specialization allows students to acquire a well-rounded knowledge of computer science that may be tailored to their individual interests. Students choose 11 upper-division computer science courses, including two project courses. This specialization will appeal to those who are interested in a broad education in computer science, or who wish to create their own unique specialization not found in the current list of other specializations under this major.

This specialization is intended to prepare students for working with and developing a wide variety of modern data and information systems. Topics covered by this concentration include database management, information retrieval, Web search, data mining, and data-intensive computing.

Intelligent Systems.

CS178: Machine Learning and Data Mining

This specialization will introduce students to the principles underlying intelligent systems, including topics such as representing human knowledge, building automated reasoning systems, developing intelligent search techniques, and designing algorithms that adapt and learn from data.

Students in this specialization will use these principles to solve problems across a variety of applications such as computer vision, information retrieval, data mining, automated recommender systems, bioinformatics, as well as individually designed projects. Networked Systems. This specialization focuses on Internet architecture, Internet applications, and network security. It also encourages students to learn about operating systems, databases, search, programming, embedded systems, and performance.How can a machine learn from experience, to become better at a given task?

How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

Machine learning as a field is now incredibly pervasive, with applications from the web search, advertisements, and suggestions to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics.

This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

There is no required textbook for the class. Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations e. You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term. Last year's lectures are also available.

Last modified February 13,at PM. Classes Group Research Publications Code. HW2Suppl. HW3Suppl. HW4Suppl. HW5Suppl.Students can post questions and collaborate to edit responses to these questions. Instructors can also answer questions, endorse student answers, and edit or delete any posted content. Piazza is designed to simulate real class discussion. It aims to get high quality answers to difficult questions, fast! The name Piazza comes from the Italian word for plaza--a common city square where people can come together to share knowledge and ideas.

We strive to recreate that communal atmosphere among students and instructors. Join Classes. Click here to log in to your other account.

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cs 178 uci

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cs 178 uci

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Computer Science (COMPSCI)

Log in Caps lock is turned on! Keep me logged in Forgot your password? Log in Cancel. University of California, Irvine change school. Are you a professor? Welcome to Piazza! Please enter your school email address Please enter the uci. Email: Confirm Email: Please enter a valid uci. Your email addresses don't match. Submit Email. We see you're new to Piazza!We will assume basic familiarity with the concepts of probability and linear algebra.

Some programming will be required; we will primarily use Python, using the libraries "numpy" and "matplotlib", as well as course code. There is no required textbook for the class. This year, we will be using Python for most of the programming in the course. Here is a simple introduction to numpy and plotting for the course; and of course you can find complete documentation for these libraries as well as many more tutorial guides online.

I usually use Python 2. Please post your questions there; you can post privately if you prefer, or if for example your question needs to reveal your solution to a homework problem.

I prefer to use Piazza for all class contact, since it enables responses by either myself, the TA, or fellow students if publicwhich should get you answers more quickly. Academic dishonesty is unacceptable and will not be tolerated at the University of California, Irvine. It is the responsibility of each student to be familiar with UCI's current academic honesty policies.

The policies in these documents will be adhered to scrupulously. Any student who engages in cheating, forgery, dishonest conduct, plagiarism, or collusion in dishonest activities, will receive an academic evaluation of "F" for the entire course, with a letter of explanation to the student's permanent file.

We seek to create a level playing field for all students. CS Machine Learning. Search this site. Course Project. Background We will assume basic familiarity with the concepts of probability and linear algebra. Textbook and Reading There is no required textbook for the class.

cs 178 uci

Python This year, we will be using Python for most of the programming in the course. Schedule See the schedule page. Academic Honesty Academic dishonesty is unacceptable and will not be tolerated at the University of California, Irvine. Syllabus Background We will assume basic familiarity with the concepts of probability and linear algebra.


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