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Curriculum

Modul CS1800-KP04

Introduction to Web and Data Science (EinfWebDat)

Duration:


1 Semester
Turnus of offer:


each winter semester
Credit points:


4 (Typ B)
Course of studies, specific field and terms:
  • Bachelor IT-Security 2016 (optional subject), interdisciplinary, Arbitrary semester
  • Bachelor Computer Science 2019 (compulsory), Canonical Specialization Web and Data Science, 1st semester
  • Bachelor Computer Science 2019 (optional subject), Introductory Module Computer Science, 1st semester
  • Bachelor Computer Science 2016 (compulsory), Canonical Specialization Web and Data Science, 1st semester
  • Bachelor Computer Science 2016 (optional subject), Introductory Module Computer Science, 1st semester
Classes and lectures:
  • Introduction to Web and Data Science (exercise, 1 SWS)
  • Introduction to Web and Data Science (lecture, 2 SWS)
Workload:
  • 20 Hours exam preparation
  • 55 Hours private studies
  • 45 Hours in-classroom work
Contents of teaching:
  • Classification vs. regression, parametric and non-parametric supervised learning
  • Networks made up of differentiable modules (Neural networks), support vector machines
  • Frequent item analysis, market basket analysis, recommendation generation
  • Statistics: samples, optimal estimators, distributions, density functions, cumulative distributions, ordinal, nominal, interval and ratio scales, confidence intervals, Pearson correlation coefficient
  • Stochastic basics, Bayesian networks for the specification of discrete distributions, queries, query response algorithms, learning methods for Bayesian networks with complete data
  • Inductive learning: version space, information theory, decision trees, rule learning
  • Ensemble methods: bagging, boosting, random forests
  • Cluster formation, K-means, analysis of variation (ANOVA), t-test, inter-cluster variation, intra-cluster variation, F-statistics, Bonferroni correction, MANOVA
  • Analysis of social structures
  • Deep Learning, Embedding Spaces
Qualification-goals/Competencies:
  • The students can explain the central ideas, define the relevant concepts and explain the functioning of algorithms with help of application scenarios for all the items listed in contents of teaching.
Grading through:
  • written exam
Responsible for this module:
Teachers:
Literature:
  • J. Stanton: An Introduction to Data Science - Syracuse University, 2013
  • Chr. Manning, P. Raghavan, H. Schütze: An Introduction to Information Retrieval - Online edition, Cambridge, UK, 2009
  • M. Welling: A First Encounter with Machine Learning - 2011
Language:
  • offered only in German
Notes:

Admission requirements for taking the module:
- None

Admission requirements for participation in module examination(s):
- Successful completion of exercises as specified at the beginning of the semester.

Module Exam(s):
- CS1800-L1: Introduction to Web and Data Science, written exam, 90min, 100% of (non-existent) module grade

Letzte Änderung:
22.2.2024