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. 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.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.Iberian express vessel owner
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.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.Marco Habegger - Up 18 North (Krüger Brother)
Classification: Advanced PDF version. Assignment 4 Programming. Chapter 9. Classification: Advanced Methods. 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.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.Spring The goal is an in-depth understanding of how an operating system manages resources in a computer and provides programmers with a machine and device independent interface. There is a difficult and time-consuming programming project GeekOS.
You are provided the source code a few thousand lines of C and some assembly of a skeleton "Unix-style" operating system kernel for a PC-like x86 platform. You have to understand a large part of this code and augment it in various ways: pipes, fork, signals, semaphores, cpu scheduling, memory paging, file system, etc.
The x86 platform is simulated by QEMU. Administrative stuff. CSIC TuTh am - pm. MW pm - pm section MW am - am section On Piazza. TA office hours. Projects and GeekOS. Course grade. To make my life easier, you will get these points only if you're just below a cutoff otherwise it doesn't matter. Available for purchase or rent from Wiley. Available from Hubetext.
Videos Neil Spring. Should require umd central authentication for box access. Excused Absence and Academic Accommodations. Recently the University approved a policy for class absence for students.
All students are expected to attend ALL classes. Any student who needs to be excused for an absence from a single lecture, recitation, or lab due to a medically necessitated absence shall:. Each note must contain an acknowledgment by the student that the information provided is true and correct. Any student who needs to be excused for a prolonged absence 2 or more consecutive class meetings or for a Major Scheduled Grading Event, the student must provide written documentation of the illness from the Health Center or from an outside health care provider.
This documentation must verify dates of treatment and indicate the timeframe that the student was unable to meet academic responsibilities.
No diagnostic information shall be given. The Major Scheduled Grading Events for this course include: Two midterms, a final and some pop quizzes.
Individual Presentation Time scheduled during the last week of class. Disability Support Accommodations. Academic Integrity.Sin 30 value
Projects are to be done individually. Consider each programming assignment to be a take-home exam. Do not expose your source code to others. Do not leave it potentially accessible to others, e. Do not look at other's source code. This also applies to code from online searchese.
Interaction via course discussion forum is permitted. Discussion of problems and code solutions is permitted as long as you do not write down code during your discussion and you wait at least 30 minutes after the end of the discussion before you write code based on the discussion.The League Championship Series "LCS" is the second year of North America's fully professional League of Legends league under partnership, and the seventh year of the league overall.Kenfield tyres durban
In the spring season, ten teams compete in a round robin group stage, with the top 6 teams continuing to playoffs. For up-to-date rosters, please see North America Roster Changes. As of Novemberto view Riot's Match History pages, log into your League of Legends account; if you are logged out, the page will display a error. Sign In.
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