This course discusses basics of the knowledge discovery process and data mining. The course focuses on three main aspects of knowledge discovery: mathematical tools, programming infrastructure for manipulating large data, and a well-defined and structured knowledge discovery process consisting of a number of interactive and iterative steps. The examples concentrate on discovering knowledge in large collections of text documents.
In recent years the amount of data that we produce increased dramatically. Very soon we will produce more data than we are able to store with the current technological solutions. Therefore, making sense out of these huge amount of data, or extracting useful, valid, understandable, and novel patterns from this data is of cruicial importance. Knowledge discovery and data mining are one of the approaches to tackle this problem. The other similar, but somewhat different approaches include database technology, machine learning, or statistics.
In this course we will investigate, analyze, and discuss a well-defined process for knowledge discovery in such a large data. Apart from the process we will also discuss the mathematics needed for data mining, as well as a new progamming methods such as Map-Reduce that have been designed to process large-scale data.
Course topics include:
In this course the students will:
At the end of this course the students will know how to:
There will be two partial examinations written within the classes. You will write the exam in the beginning of a lecture for 45 minutes. Each partial examination will have 3 questions with difficulty adjusted to solve both problems in approx. 35 minutes. You can get max 15 points for each question resulting in a total of 90 points.
Appart from the partial examinations there will be a standard written examination at the end of the course. 4 questions with max 20 points for each question. The total number of points that can be reached will be 80.
The grading scheme is as follows:
Points from study year: 2016