Module Manual

Modul CS4337-KP12

Bio-Inspired Computing (BioInCo)


1 Semester
Turnus of offer:

every summer semester
Credit points:

Course of studies, specific field and terms:
  • Master Artificial Intelligence 2023 (compulsory), Artificial Intelligence, 1st or 2nd semester
Classes and lectures:
  • Machine Learning Lab (practical course, 2 SWS)
  • Collective Robotics (lecture, 2 SWS)
  • Foundations of Machine Learning and Data Science (lecture, 2 SWS)
  • Evolutionary Robotics (lecture, 2 SWS)
  • 240 Hours private studies
  • 90 Hours e-learning
  • 30 Hours work on project
Contents of teaching:
  • Foundations of Machine Learning and Data Science: Classification, regression, prediction: perceptrons, multi-layer perceptrons, and deep learning / Statistical principles: sampling, estimators, distribution, density, cumulative distribution, scales: nominal, ordinal, interval, and ratio scales, hypothesis testing, confidence intervals / Stochastic foundations, probabilities, Bayesian networks for the specification of discrete distributions, queries, query answering algorithms, learning procedures for Bayesian networks / Time series analysis: autoregression, integration, moving average (ARIMA), ordinal patterns, permutation entropy features, dynamic Bayesian networks and associated machine learning techniques / Inductive learning: version space, information theory, decision trees, rule learning / Ensemble methods, bagging, boosting, random forests / Automated machine learning / Clustering, k-means, analysis of variation (ANOVA), T-test, inter-cluster variation, intra-cluster variation, F-statistics, Bonferroni correction, MANOVA.
  • Evolutionary Robotics: Biological basics of natural evolution / Evolutionary computation and optimization: coding, search spaces, genetic operators / Conducting evolutionary experiments with mobile robots in hardware and in simulation / Robot simulations and the reality gap / Concepts of reactive behavior and how to go beyond / Explanation of evolutionary dynamics in terms of nonlinear dynamics / Heuristic and empirical approach in robot experiments / Modular robotics for evolution of robot morphologies / Intensive discussion of state of the art methods, such as bridging the reality gap, novelty search, MAP elites, etc.
  • Collective Robotics: Self-organization and feedback loops in systems / Basics of swarm behaviors, swarm robotics and behavior-based robotics / Robot swarms on land, water and in the air / Self-organized coordination of robots, autonomous assignment of tasks and roles, online distribution of tasks / Collective behaviors limited by local information, representative samples / Synchronization, estimate group size, mathematical modeling, micro-macro problem, random graphs / Collective decision making, urn models, opinion dynamics, speed vs accuracy tradeoff / Bio-hybrid robotics: animals and robots, plants and robots, cyborgs
  • Machine Learning Lab: Methods and algorithms for the visualization, analysis and generation of medical image data, including current research work in the field of medical image processing / Basics of medical image processing – visualization and pre-processing of images / Image data augmentation techniques / Basics of connectionist networks in medical image processing / Convolutional networks and deep learning in medical image processing / U-Nets and generative adversarial networks (GANs) for the generation of medical image data / Generative models for medical image processing
  • For all topics listed in the course content under the bullet points, students will be able to name the central ideas, define the relevant terms in each case, and explain how associated algorithms work using examples of applications.
Grading through:
  • portfolio exam
Responsible for this module:
  • S. Nolfi, D. Floreano: Evolutionary Robotics - MIT Press, 2001
  • H. Hamann: Swarm Robotics: A Formal Approach - Springer, 2018
  • M.P. Deisenroth, A.A. Faisal, C.S. Ong: Mathematic of Machine Learning - Cambridge University Press, 2020
  • S.J. Russell, P. Norvig: Artificial Intelligence: A Modern Approach - 4th Ed., Pearson, 2020
  • M. Kaptein, E. van den Heuvel: Statistics for Data Scientists: An Introduction to Probability, Statistics, and Data Analysis - Springer, 2022
  • offered only in English

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- 50% of online quiz points

Module exam(s):
CS4337-L1:Bio-Inspired Computing portfolio exam for a total of 100 points, divided as follows:
- 50 points for an e-test (oral or written).
- 50 points for a project presentation

Letzte Änderung: