Instructor | David Rosenberg |
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Lecture | Wednesdays 5:10pm–7pm, Warren Weaver Hall 109 |
Lab | Thursday 6:10pm–7pm, Warren Weaver Hall 109 |
Office Hours | Instructor: Thursday 7pm–8pm, Warren Weaver Hall 109 |
Graders: Monday 2pm–4pm / Tuesday 1pm–2pm, CDS common area |
Tikhonov, Ivanov, Square Hinge, and GLMs
Due: April 21st, 4pm
This course covers a wide variety of topics in machine learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve both the traditional and the novel data science problems found in practice. This course will also serve as a foundation on which more specialized courses and further independent study can build.
This is a required course for the Center for Data Science's Masters degree in Data Science, and the course is designed for the students in this program. Other interested students who satisfy the prerequisites are welcome to take the class as well. Note that this class is a continuation of DS-GA-1001 Intro to Data Science, which covers some important, fundamental data science topics that may not be explicitly covered in this class (e.g. data cleaning, cross-validation, decision trees, and sampling bias).
Course details can be found in the syllabus.
This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the Lab Instructor, graders, and the Instructor. Rather than emailing questions to the teaching staff, you are encouraged to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com. Without registering, you can also view an anonymized version of our Piazza board.
See the Course Calendar for all schedule information.
For registration information, please contact Varsha Tiger.
Problem sets (60%) + Midterm exam (20%) + Project (20%)
Up to (2%) extra credit can be earned by answering student questions on Piazza and for positive contributions to class and lab discussions; there will be additional extra credit opportunities in the homework assignments in the form of optional problems and competitions.
The course conforms to NYU’s policy on academic integrity for students.
(If you find additional references that you recommend, please share them on Piazza and we can add them here.)
Homework Submission: Homework should be submitted through NYU Classes.
Late Policy: Homeworks are due at 4pm on the date specified. Homeworks will still be accepted for 48 hours after this time but will have a 20% penalty.
Collaboration Policy: You may discuss problems with your classmates. However, you must write up the homework solutions and the code from scratch, without referring to notes from your joint session. In your solution to each problem, you must write down the names of any person with whom you discussed the problem—this will not affect your grade.
Shanshan is a research data scientist in the YP Mobile Labs group at YP.
Hao is a second year student in CDS Data Science Masters program. In the fall he will begin as a data scientist at AIG.
Ran is currently a second year student in the Data Science program at NYU. She will begin working on the AIG science team in Summer 2015.
Gideon is currently the Head of Data Science in the CTO office at Bloomberg LP.
Kurt is a researcher at the quantitative hedge fund PDT Partners.
Kush is a research staff member at IBM Research and a data ambassador with DataKind.
Brian is VP of Data Science at Dstillery and Adjunct Professor of Data Science at NYU Stern School of Business.