636-0018-00L Data Mining I
Semester | Herbstsemester 2019 |
Dozierende | K. M. Borgwardt |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Lehrveranstaltungen
Nummer | Titel | Umfang | Dozierende | |||||||
---|---|---|---|---|---|---|---|---|---|---|
636-0018-00 G | Data Mining I Tutorial: 8-9h, Lecture: 9-11h. The tutorial and lecture will be held each Wednesday in Basel and will be transmitted via videoconference to Zurich. ATTENTION: Course starts on Wednesday, October 2! Course will be streamed and recorded | 3 Std. |
| K. M. Borgwardt | ||||||
636-0018-00 A | Data Mining I Project Work (compulsory continuous performance assessment), no fixed presence required. | 2 Std. | K. M. Borgwardt |
Katalogdaten
Kurzbeschreibung | Data Mining, the search for statistical dependencies in large databases, is of utmost important in modern society, in particular in biological and medical research. This course provides an introduction to the key problems, concepts, and algorithms in data mining, and the applications of data mining in computational biology. |
Lernziel | The goal of this course is that the participants gain an understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications. |
Inhalt | The goal of the field of data mining is to find patterns and statistical dependencies in large databases, to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses. In this course, we will present the algorithmic foundations of data mining and its applications in computational biology. The course will feature an introduction to popular data mining problems and algorithms, reaching from classification via clustering to feature selection. This course is intended for both students who are interested in applying data mining algorithms and students who would like to gain an understanding of the key algorithmic concepts in data mining. Tentative list of topics: 1. Distance functions 2. Classification 3. Clustering 4. Feature Selection |
Skript | Course material will be provided in form of slides. |
Literatur | Will be provided during the course. |
Voraussetzungen / Besonderes | Basic understanding of mathematics, as taught in basic mathematics courses at the Bachelor's level. |
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
ECTS Kreditpunkte | 6 KP |
Prüfende | K. M. Borgwardt |
Form | Sessionsprüfung |
Prüfungssprache | Englisch |
Repetition | Die Leistungskontrolle wird nur in der Session nach der Lerneinheit angeboten. Die Repetition ist nur nach erneuter Belegung möglich. |
Prüfungsmodus | schriftlich 90 Minuten |
Zusatzinformation zum Prüfungsmodus | Final grade: 70% written examination, 30% project work Project work has to be re-done in case of repetition The course includes up to 6 compulsory continuous performance assessments in form of biweekly homework assignments, which constitute 30% of the final grade |
Hilfsmittel schriftlich | Keine |
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan. |
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar. | |
Es werden nur die öffentlichen Lernmaterialien aufgeführt. |
Gruppen
Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
Keine zusätzlichen Belegungseinschränkungen vorhanden. |
Angeboten in
Studiengang | Bereich | Typ | |
---|---|---|---|
Biotechnologie Master | Biomolekulare Orientierung | W | |
Biotechnologie Master | System-Orientierung | W | |
Computational Biology and Bioinformatics Master | Data Science | W |