Download Course and Examination Fact Sheet

Course and Examination Fact Sheet: Spring Semester 2015
10,824: Data Mining
ECTS credits: 4
Overview examination/s
(binding regulations see below)
Decentral ‑ Written examination (100%)
Attached courses
Timetable ‑‑ Language ‑‑ Lecturer
10,824,1.00 Data Mining ‑‑ English ‑‑ Stine Robert A. Course information
Course prerequisites
Familiarity with regression modeling, both practical and theoretical (to the extent of understanding regression inference). Practical experience building regression models with software from moderate to large data sets. Some exposure to math stat
would be helpful but not necessary.
Course content
The course follows the text An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani. The course begins by
covering the exploratory use of regression models and issues of variable selection. The material then moves to alternative
techniques such as the lasso and neural networks.
Course structure
Lecture with some hands on, in class examples and exercises.
Course literature
A suitable textbook is not available. Possible candidates include:
Principles of Data Mining (2001). Hand, Mannila, and Smyth
Data Mining (2011, 2nd edition). Kantardzic
Elements of Statistical Learning (2009, free on‑line. An introductory version is currently in preparation). Hastie, Tibshirani, and
Friedman
Additional course information
‑‑
Examination information
Fact sheet version: 3.0 as of 01/21/2015, valid for Spring Semester 2015
Page 1 / 3
Examination sub part/s
1. Examination sub part (1/1)
Examination time and form
Decentral ‑ Written examination (100%)
Remark
Written assignments at the end of the course
Examination­aid rule
Open Book
Students are free to choose aids but will have to comply with the following restrictions:
At such examinations, all the pocket calculators of the Texas Instruments TI‑30 series are admissible. Any other pocket
calculator models are inadmissible.
In addition, any type of communication, as well as any electronic devices that can be programmed and are capable of
communication such as electronic dictionaries, notebooks, tablets, PDAs, mobile telephones and others, are inadmissible.
Students are themselves responsible for the procurement of examination aids.
Supplementary aids
‑‑
Examination languages
Question language: English
Answer language: English
Examination content
Written assignment at the end of the course.
Assignment is open book, open notes. Assignment will require students to complete exercises that review the material covered
each day of class. The final exam project will require – in addition to traditional short answer questions – that students analyze a
data set, generating predictions. The accuracy of these predictions will feature prominently in the final grade.
Examination relevant literature
A suitable textbook is not available. Possible candidates include
Principles of Data Mining (2001) Hand, Mannila, and Smyth
Data Mining (2011, 2nd Edition) Kantardzic
Elements of Statistical Learning (2009, free on‑line. An introductory version is currently in preparation). Hastie,
Tibshirani, and Friedman
Fact sheet version: 3.0 as of 01/21/2015, valid for Spring Semester 2015
Page 2 / 3
Please note
We would like to point out to you that this fact sheet has absolute priority over other information such as StudyNet,
faculty members’ personal databases, information provided in lectures, etc.
When will the fact sheets become binding?
Information about courses and examination time (central/decentral and grading form): from the start of the bidding
process on 22 January 2015
Information about decentral examinations (examination‑aid rule, examination content, examination relevant
literature): after the 4th semester week on 16 March 2015
Information about central examinations (examination‑aid rule, examination content, examination relevant
literature): from the start of the enrolment period for the examinations on 6 April 2015
Please look at the fact sheet once more after these deadlines have expired.
Fact sheet version: 3.0 as of 01/21/2015, valid for Spring Semester 2015
Page 3 / 3