Lecture time: TTh pm. Contact: zshi22 uic. The course will introduce common machine learning tasks, such as classification and clustering, and some of the successful machine learning techniques and broader paradigms that have been developed for these tasks. Topics include but are not limited to decision trees, nearest neighbors, linear models, support vector machines, neural networks, ensemble methods, k-means, and graphical models.
The course is programming-intensive and an emphasis will be placed on tying machine learning techniques to specific real-world applications through hands-on experience. Working knowledge of probability, linear algebra, calculus, and ability to learn to program in Python. Important date changed : There will be a quiz on prerequisites during the third lecture period Tuesday, January 23 to help you assess whether this course is for you.
Anyone considering to take this course should come to class and take the quiz even if they are not registered yet.
We will use Piazza for the course schedule, discussions, and materials. Students registered for the course will be sent an enrollment email before the first day of class.
Python is the programming language used for homework assignments.
Code on github. Toggle navigation. Description This course provides an introduction to machine learning, the study of systems that improve automatically based on data and past experience. Prerequisites Working knowledge of probability, linear algebra, calculus, and ability to learn to program in Python. Course materials We will use Piazza for the course schedule, discussions, and materials.Lecture time: MW pm. Contact: mli uic.
This transformation has led to the emergence of data science as a new discipline. The explosive growth of interest in this area has been driven by research in social, natural, and physical sciences with access to data at an unprecedented scale and variety, by industry assembling huge amounts of operational and behavioral information to create new services and sources of revenue, and by government, social services and non-profits leveraging data for social good.
This emerging discipline relies on a novel mix of mathematical and statistical modeling, computational thinking and methods, data representation and management, and domain expertise. This course provides an in-depth overview of data science from a computer science perspective. Topics include modeling, storage, manipulation, integration, classification, analysis, visualization, information extraction, and big data. The course is programming-intensive and an emphasis will be placed on tying data science concepts to specific real-world applications through hands-on experience.
Working knowledge of probability, data structures and algorithms, and ability to learn to program in Python. We will use Piazza for the course schedule, discussions, and materials, and Gradescope for grading. Python is the programming language used for homework assignments. We will use Google Cloud for big data computing thanks to a generous grant that Google Cloud provided for this course.
No textbook is required. Readings will be assigned, using multiple online sources, including: [PTDS] Principles and techniques of data science. Lau, Gonzalez, Nolan. Leskovec, Rajaraman, Ullman. Adhikari, DeNero. Hal Daume III. Toggle navigation.Massey Ferguson 7618 hauling a Grimme CS1500 Combistar De Stoner
Prerequisites Working knowledge of probability, data structures and algorithms, and ability to learn to program in Python. Course materials We will use Piazza for the course schedule, discussions, and materials, and Gradescope for grading. Data science in the real world Invited talk by Dr.The schedule will be modified slightly based on the progress of the class.
Thanks for your careful reading. Chapter 1. Chapter 2. Know Your Data. Assignment 1. Chapter 2 Additional Material. Chapter 3. Data Preprocessing. Chapter 4. Chapter 5. Data Cube Technology.
Chapter 6. Ch ap. Chapter 7 additional materials sequential pattern mining. Chapter 8. Classification: Basic Concepts. Classification: Advanced PDF version. Assignment 4 Programming. Chapter 9. Classification: Advanced Methods.
CS 412 : Introduction to Data Mining P3P4
Assignment 5. Clustering: Basic PDF version. Chapter Cluster analysis: Basic Concepts. A sample final exam. Trends and Research Frontiers in Data Mining. CS Pages Blog. Child pages.
Course Syllabus and Schedule. Browse pages. A t tachments 2 Page History Scaffolding History. Copy with Scaffolding XML. Dashboard Home. Jira links. Created by Han, Jiaweilast modified on Dec 07, No labels.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.
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University of Illinois at Urbana-Champaign change school. Are you a professor? Welcome to Piazza! Please enter your school email address Please enter the illinois. Email: Confirm Email: Please enter a valid illinois. Your email addresses don't match. Submit Email.This course is designed to challenge you as a programmer and new computer scientist at the University of Illinois at Urbana-Champaign.
Rather than the sand-boxed, contained, and simple problems of your previous courses that used significant scaffolding and pre-built libraries, you will be interacting with a much more complex environment: the entire system and even computing networks. You will need to fully understand how memory is allocated, used, and re-used within a process. You will also need to know how input and output can be optionally buffered between processes and files. In short, it is time to remove the training wheels off and instead fling open the doors, welcoming you to the big, wide world of computing.
Oh, and did we mention the challenge of concurrency and solving asynchronous problems, so that your program can take advantage of the multi-core CPU inside each machine? You can find a full list of times and locations on the calendar. Please read our Piazza policy, before posting. CS Data Structures. CS Computer Architecture.Project 1: Disk vs In-Memory vs Database. The project consists in three parts. In each part you are asked to implement the backend of the application in a different way filesystem, in-memory data structures and a embedded database.
The goal of this practice project to give you the opportunity to experience with SQL. In case, you have taken CSthe project will help you to practice the concepts already learned in that course.
In both cases, we encourage you to complete this project. The goal of this project is to implement a simplified version of a Buffer Manager layer, without support of concurrency control or recovery. The goal of this project is to implement Relational Algebra Operators using the Iterator interface inside a working database system.
The goal of this project is getting some experience in Big Data Technologies. For the project, you will use Hadoop Distributed Filesystem and Spark to perform some data processing tasks. We encourage you to start as soon as possible with this project, especially since you need to become familiar with the Purdue Scholar Cluster.
Related material will be covered during the PSO sessions of April. The goal of this project is to implement a simplified version of the 2PL protocol used to handle concurrent operations e. The implementation should prevent deadlocks and handling them properly.
Even though the project is non-graded, we encourage you to complete it, to get some practical experience about Chapter 20 Intro to Transaction Processing Concepts and Theory21 Concurrency Control Techniques and 22 Database Recovery Techniques from the Textbook. The project will be covered during the PSO sessions in April. No solution will be posted online.Busy professionals can complete a degree online while they continue to meet their current life and career obligations.
The Online MCS program is a non-thesis coursework-only degree that requires 32 credit hours of graduate coursework, completed through eight graduate-level courses each at the four credit hour level. The MCS requires that four of these eight courses are chosen from unique "core" areas of computer science, and that three of these eight courses must be at the advanced graduate level level.
The Online MCS currently offers coursework in the core areas of artificial intelligence, databases, human-computer interaction, software engineering, scientific computing and high-performance computing.
Who may apply? Applicants should hold a 4-year bachelor's degree or equivalent. The recommended undergraduate GPA for applicants applying to the Professional Master's program is a 3. Applications for the MCS do not require letters of recommendation, but they will be considered if included, especially if used to justify experience in lieu of required coursework, or other irregularities. Printable version. Must complete four courses 16 credit hours each from a different area, from the following core areas with a grade of B- or higher.
CS Numerical Analysis Fall.
Principles of Data-Intensive Systems
The Department of Computer Science does not offer research or teaching assistantships to students enrolled in our online programs, including the Online MCS.
Apply by May 30 for Fall admission! Apply Now. CS Calendar. Follow Us on Facebook. Follow Us on Twiitter. Follow Us on Youtube. Follow Us on LinkedIn. Follow Us on Instragram. CS Parallel Computing Spring.