{"id":92,"date":"2024-10-18T10:14:10","date_gmt":"2024-10-18T08:14:10","guid":{"rendered":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/?page_id=92"},"modified":"2024-11-28T08:02:44","modified_gmt":"2024-11-28T07:02:44","slug":"curriculum","status":"publish","type":"page","link":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/my-studies\/curriculum\/","title":{"rendered":"Curriculum"},"content":{"rendered":"\n
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Current Curriculum<\/h2>\n <\/div>\n
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\n 1. Semester<\/span>\n \n\n<\/svg> <\/h4>\n <\/a>\n \n \n
\n \n Data Storage and Processing 1 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807105<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Databases and Query Languages<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Introduction and basic concepts\n– Overview of database models \n– Database architectures\n– Data security\n– …\n\nRelational databases\n– Basic concepts of the relational data model\n– Structured Query Language (SQL)\n– Import\/export of datasets\n– Data integrity, normal forms and denormalisation\n– Access control and user administration\n– Advanced topics in relational databases\n– …\n\nOverview of non-relational databases\n– Key-value databases and document-oriented databases\n– Multidimensional databases\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Data Storage and Processing 2 |\n Practical\n (UE)\n | Coursecode:\n M250807106<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Programming and Data Processing<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Programming \/ Scripting\n– Programming paradigms\n– Basics of programming and scripting\n– Operators and control structures\n– Functions and libraries\n– Regular expressions\n– …\n\nData-based applications\n– Importing and exporting data\n– Elementary data handling \n– …\n\nTools\n– Version control systems\n– Clean coding and debugging\n– Integrated development environments\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Introduction and Foundations 1 |\n Lecture\n (VO)\n | Coursecode:\n M250807101<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Introduction to Data Science and Artificial Intelligence<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Introduction to Data Science and Artificial Intelligence\n– Overview of the field\n– Possible applications and areas of use\n– Social and legal context\n– Future perspectives\n– …\n\nBasic terms and overview of data science and artificial intelligence\n– Basic terms of data science and artificial intelligence\n– Overview of important methods \n– Biases and metrics\n– …\n\nTools for data science and artificial intelligence\n– Overview of IT infrastructures\n– Overview of tools and frameworks\n– Overview of scripting and programming languages\n– Overview of software stacks and development methods\n…\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Introduction and Foundations 1 |\n Practical\n (UE)\n | Coursecode:\n M250807102<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Quick Guide to Machine Learning<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Examples of unsupervised learning\n– Practical introductory examples from the field of clustering\n– Practical introductory examples from the field of dimensionality reduction\n– …\n\nExamples of Supervised Learning\n– Practical examples from the field of classification\n– Practical introductory examples from the field of regression\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Introduction and Foundations 2 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807103<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Probability Theory and Descriptive Statistics<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Descriptive statistics\n– Basic terms and areas of application of statistics\n– Frequencies, frequency distributions and empirical distribution functions\n– Parameters of empirical distributions\n– Multivariate empirical distributions\n– Visualisation of statistical data\n– …\n\nProbability theory \n– Basic terms of probability theory\n– Conditional probabilities and Bayes’ theorem\n– Basic terms of combinatorics\n– One and multidimensional random variables\n– Important discrete and continuous univariate distributions \n– Limit theorems and approximation of distributions\n– …\n\n Introduction to Bayes statistics\n– A-priori and A-posteriori distributions\n– Update and analysis of distributions for parameters\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Introduction and Foundations 3 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807104<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Modelling and Numerical Simulations<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Modeling and simulation\n– Basic concepts of systems, modeling and simulation\n– Mathematical foundation of modeling and simulation\n– Overview of modeling and simulation types\n– …\n\nGraph theory\n– Basic concepts of graphs\n– Characteristics and parameters of graphs\n– Main types of graphs and networks\n– …\n\nSystem dynamics\n– Causal graphs, pulse models and Markov chains\n– Fundamentals of cybernetics and control engineering\n– Ordinary differential equation systems and numerical integration\n– …\n\nMonte Carlo simulations\n– Basic concepts of Monte Carlo simulations\n– Usage of Monte Carlo simulations for forecasting\n– …\n <\/div>\n\n <\/div>\n <\/button>\n
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Elective Subjects:<\/h5>\n <\/div>\n \n
\n \n Refresher of Foundations |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807109<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Refresher on Information Technology<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Hardware\n– Central components of a computer\n– Bus systems and peripherals as well as processor architectures\n– Information processing and information encoding\n– …\n\nOperating systems\n– Families of operating systems and their structure\n– Process and storage management\n– File systems and object storage\n– …\n\nNetworks\n– Introduction to the functioning of networks\n– Introduction to the terminology of networks\n– OSI layer model and TCP\/IP\n– Interface protocols\n– …\n\nCryptography\n– Basic concepts of cryptography\n– Symmetric and asymmetric cryptographic methods\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Refresher of Foundations |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807107<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Refresher on Mathematics<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Basic mathematical concepts\n– Set theory and number sets \n– Calculating in the complex number system\n– Elementary functions\n– …\n\nElementary analysis\n– Differential calculus, curve sketching and extreme value problems\n– Fundamentals of integral calculus\n– …\n\nBasic concepts of linear algebra\n– Vectors, matrices and tensors\n– Determinants and solutions of linear systems of equations\n– Eigenvalues and Eigenvectors\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Refresher of Foundations |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807108<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Refresher on Unix Shells and LaTeX<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Basics of unixoid operating systems\n– Introduction to text-based shells \n– File types and data compression\n– File systems and permissions\n– Processes and jobs\n– Important tools of unixoid shells\n– Shell scripting\n– …\n\nText manipulation\n– Elementary tools and pipes \n– Regular expressions\n– Important editors\n– …\n\nLaTeX markup language\n– Commands and environments\n– Document classes and page styles\n– Mathematical formulas\n– …\n <\/div>\n\n <\/div>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n
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\n 2. Semester<\/span>\n \n\n<\/svg> <\/h4>\n <\/a>\n \n \n
\n \n Computational Intelligence 1 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807202<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Data Mining and Statistical Learning<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Basics of multivariate statistics\n– Repetition of matrix algebra\n– Multivariate observations and distributions\n– Covariance, correlation and distance matrices\n– …\n\nMethods of supervised learning\n– Simple and multiple linear regression \n– Regularization methods\n– Methods based on latent variables\n– Decision trees and related methods\n– Logistic regression\n– Linear and squared discriminant analysis\n– Naive-Bayes-classifier\n– K-Nearest-Neighbor Classification\n– …\n\nMethods of Unsupervised Learning\n– Methods of Cluster analysis \n– Dimension reduction\n– …\n\nFurther topics\n– Optimization and validation of prediction models\n– Advanced forms of visualization\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Computational Intelligence 1 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807203<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Numerics and Computational Optimisation<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Technical numerics\n– Reduction of numerical errors\n– Numerical solution of equation systems\n– Numerical differentiation and numerical integration\n– Numerical solution of ordinary differential equation systems\n– …\n\nNumerical optimization\n– Fundamental aspects of optimization tasks\n– Linear programming\n– Nonlinear optimization with constraints\n– Multi-criteria optimization\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Computational Intelligence 2 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807204<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Machine Learning with Artificial Neural Networks<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Basics and tools of artificial neural networks\n– Artificial neurons, activation functions, layers (esp. MLP)\n– Backpropagation and error minimisation\n– Introduction to common technical frameworks\n– Areas of application and limitations\n– …\n\nArchitectures of artificial neural networks\n– Overview of further architectural building blocks\n– Recurrent Networks\n– Convolutional Networks\n– Autoencoder Networks\n– …\n\nSelected simple application areas\n– Text analysis\n– Image and video analysis\n– Audio and signal analysis\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Data Storage and Processing 3 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807201<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Data Quality and Data Fusion<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Data preparation\n– Data type conversion and normalisation\n– Analysis of data quality and detection of anomalies\n– Singular and multiple imputation\n– Anonymisation and hashing of data\n– …\n\nPreparation of complex data\n– Special features of audio, image, video and signal data\n– Transformations and discretisation of continuous data\n– …\n\nData merging and feature engineering\n– Complex filtering and sorting of data\n– Aggregations and transformations of data\n– Feature selection methods\n– …\n\nPipelining and data lifecycle management\n– Basic terms and overview of frameworks\n– Design and implementation of pipelines\n– Basic elements of data lifecycle management\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Interdisciplinary Qualifications 1 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807205<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Project Management and Scientific Writing<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Basics of project management\n– Basic concepts and tools\n– Elements of classic project management\n– Agile project management\n– Work techniques and time management\n– …\n\nResearch processes\n– Important theories and schools of thought in the philosophy of science\n– Overview of scientific research methods\n– Research and scientific excerpting\n– Research questions, hypotheses, research projects\n– Analysis, publication and presentation of findings\n– …\n\nBasics of scientific writing\n– Formulation, structuring, formatting and visualization\n– Reference management, property rights and ethical principles\n– Quality assurance or reviews and peer reviews\n– Publication platforms, rankings and impact factors\n– …\n\nConsolidation of one’s own Master’s thesis\n– Finding a topic for one’s own Master’s thesis \n– Deriving research questions for one’s own Master’s thesis\n– …\n <\/div>\n\n <\/div>\n <\/button>\n
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Specialisation Industrial Data Science and AI-based Optimisation:<\/h5>\n <\/div>\n \n
\n \n Industrial Data Science and AI-based Optimisation 1 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807213<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Digital Signal Processing<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Audio and signal processing\n– Spectral Analysis\n– Noise Reduction and Cancellation\n– Types of audio and signal compression\n– Introduction to speech recognition\n– Introduction to real-time data analysis\n– …\n\nImage processing\n– Application of different filters\n– Types of image compression\n– Object recognition, classification and tracking\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Industrial Data Science and AI-based Optimisation 1 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807212<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Industrial Robotics and Control Engineering<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Fundamentals of robotics\n– Overview of different robot architectures\n– Overview of key sensors and their signals\n– Overview of essential actuators and their control\n– Degrees of freedom, kinematics and inverse kinematics\n– …\n\nControl engineering\n– Control vs. regulation\n– Introduction to sensor fusion and the Kalman filter\n– Control of dynamic systems\n– Order reduction of system models\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Industrial Data Science and AI-based Optimisation 2 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807214<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Time Series Analysis and Inferential Statistics<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Time series analysis\n– Special concepts of time series analysis\n– Descriptive analysis and visualisation of time series\n– Feature engineering and modeling techniques \n– Anomaly detection, filtering and quality\n– Forecasting and imputation\n– …\n\nInductive statistics\n– Population and samples\n– Point and interval estimation\n– Estimation using the maximum likelihood and method-of-moments\n– Parametric and non-parametric statistical tests\n– Multiple testing\n– Computationally intensive methods\n– …\n <\/div>\n\n <\/div>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n
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\n 3. Semester<\/span>\n \n\n<\/svg> <\/h4>\n <\/a>\n \n \n
\n \n Master's Thesis and Master's Examination 1 |\n Seminar\n (SE)\n | Coursecode:\n M250807301<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Master's Thesis Expos\u00e9<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Iterative writing of the expos\u00e9 for the Master’s thesis\n– Presentation of the problem and the research questions\n– Presentation of the methodology and the planned approach\n– Comprehensive presentation of the relevant literature and previous scientific work\n– Observance of norms, standards, templates and guidelines of the degree program\n– …\n\nPresentation of the contents of the expos\u00e9\n– Scientific presentation according to templates and specifications of the degree program\n– Discussion and defense of the planned procedure \n– Revision of the expos\u00e9 based on the feedback and peer reviews received\n– …\n <\/div>\n\n <\/div>\n <\/button>\n
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Elective Subjects:<\/h5>\n <\/div>\n \n
\n \n Elective Lab Course |\n Project Thesis\n (PA)\n | Coursecode:\n M250807322<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Augmented and Virtual Reality Lab<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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2.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Fundamentals of augmented and virtual reality\n– Fundamentals of augmented reality and virtual reality\n– Discussion of current developments\n– …\n\nImplementation of projects in the field of augmented and virtual reality\n– Analysis of given requirements\n– Development of different solution strategies\n– Planning, development and programming of an AR or VR project \n– Management and controlling of the project and project progress\n– Collaboration within the team including any conflict resolution\n– …\n\nProject documentation and dissemination of project results\n– Creation of project documentation based on norms, standards and specifications\n– Presentation and discussion of project progress and project results\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Elective Lab Course |\n Project Thesis\n (PA)\n | Coursecode:\n M250807320<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Distributed Computing and Federated Learning<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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2.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Fundamentals of distributed computing and federated learning\n– Fundamentals of distributed computing and federated learning\n– Discussion of current developments\n– …\n\nImplementation of projects in the field of distributed computing and federated learning\n– Analysis of given requirements\n– Development of different solution strategies\n– Planning, implementation, management and controlling of the project or project progress\n– Collaboration in the team incl. possible conflict resolution\n– …\n\nProject documentation and dissemination of project results\n– Creation of project documentation based on norms, standards and specifications\n– Presentation and discussion of project progress and project results\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Elective Lab Course |\n Project Thesis\n (PA)\n | Coursecode:\n M250807321<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Tangible UI\/UX Lab<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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2.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Fundamentals of tangible UI\/UX\n– Fundamentals of tangible user interfaces and UI\/UX design\n– Discussion of current developments\n– Selected basics of microelectronics\n– …\n\nImplementation of projects in the field of tangible UI\/UX\n– Analysis of given requirements\n– Development of different solution strategies\n– Planning, development and programming of a tangible UI\/UX project\n– Management and controlling of the project or project progress\n– Collaboration in the team incl. possible conflict resolution\n– …\n\nProject documentation and dissemination of project results\n– Creation of project documentation based on norms, standards and specifications\n– Presentation and discussion of project progress and project results\n– …\n <\/div>\n\n <\/div>\n <\/button>\n
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Specialisation Industrial Data Science and AI-based Optimisation:<\/h5>\n <\/div>\n \n
\n \n Industrial Data Science and AI-based Optimisation 3 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807314<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Advanced Prediction Methods<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Predictive maintenance\n– Overview of use cases and methods used\n– Planning, design, setup, training and optimisation of models\n– …\n\nState-of-the-art methods of predictive analytics \n– Recent developments in the field\n– Discussion and application of current procedures and methods\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Industrial Data Science and AI-based Optimisation 3 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807315<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Agent-based Programming and Reinforcement Learning<\/h5>\n
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5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
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3.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Fundamentals of agent-based programming\n– Cellular automata, self-organisation and emergences \n– Characteristics of agents and agent-based models\n– Introduction to the design and programming of agent-based models\n– Optimisation of agent-based models using evolutionary methods\n– Georeferencing and conversion of spatial coordinates\n– Swarm intelligence and synchronisation\n– Basics of discrete event simulations\n– …\n\nDecision and game theory\n– Fundamentals of decision theory\n– Game theory basics \n– Markov decision problems\n– …\n\nReinforcement Learning\n– Basic concepts and overview of methods\n– Temporal Difference Learning (Q-Learning, SARSA, …)\n– Basic concepts and fundamentals of MARL\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Industrial Data Science and AI-based Optimisation 4 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807318<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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MLOps and Continuous Monitoring<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Foundations of MLOps\n– Principles of MLOps\n– Technology stacks and tools\n– Use case analysis and derivation of pipeline architectures\n– …\n\nLifecycle of machine learning models\n– Training and optimisation of models\n– Traceability of model-related experiments\n– Code, data and model verification\n– Validation and quality assurance of models\n– Operational use of machine learning models\n– …\n\nMonitoring and further development\n– Design, planning and implementation of CI\/CD monitoring\n– Design, planning and implementation of application monitoring\n– Ongoing training of the models\n– Ongoing further development of the pipeline\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Industrial Data Science and AI-based Optimisation 5 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807317<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Biologically Inspired Approaches to Optimisation<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
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1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Heuristic or metaheuristic optimization\n– Differentiation from classical optimisation\n– Introduction to evolutionary algorithms (EA)\n– Genetic algorithms (GA) and different variations\n– …\n\nSelected advanced metaheuristics\n– Simulated Annealing\n– Ant Colony Optimisation (ACO)\n– Particle Swarm Optimisation (PSO)\n– Combinatorial Optimisation\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Industrial Data Science and AI-based Optimisation 5 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807316<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Process Optimisation and AI-based Operations Research<\/h5>\n
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2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
\n
1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Mathematical basics\n– Repetition and specialisation of selected topics of linear algebra \n– Repetition and specialisation of graph theory fundamentals\n– …\n\nGraph-based optimisation\n– Scheduling and allocation problems\n– Round trip problems and routing problems\n– Further graph-based optimisations\n– …\n\nOperations Research\n– Typical problems of operations research\n– Dynamic optimisation problems\n– Advanced discrete optimisation problems\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Industrial Data Science and AI-based Optimisation 6 |\n Project Thesis\n (PA)\n | Coursecode:\n M250807319<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
\n
Project Work in Industrial Data Science and AI-based Optimisation<\/h5>\n
\n
\n
5.00<\/h5>\n ECTS<\/span>\n <\/div>\n
\n
2.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Implementation of projects in the field of \u201cIndustrial Data Science and AI-based Optimization\u201d\n– Analysis of given requirements\n– Development of different solution strategies\n– Planning, implementation, management and controlling of projects\n– Collaboration in a team including possible conflict resolution\n– …\n\nProject documentation and dissemination of project results\n– Creation of project documentation based on norms, standards and specifications\n– Presentation and discussion of project progress and project results\n– …\n <\/div>\n\n <\/div>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n \n

\n 4. Semester<\/span>\n \n\n<\/svg> <\/h4>\n <\/a>\n \n \n
\n \n Interdisciplinary Qualifications 2 |\n Seminar\n (SE)\n | Coursecode:\n M250807402<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
\n
Business Dialogue and Case Studies<\/h5>\n
\n
\n
2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
\n
1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Data science and AI in practice\n– Analysis of problems and selection of suitable methods and algorithms\n– Discussion of the advantages and disadvantages of different methods and algorithms\n– …\n\nBest practices and the future of data science and AI projects\n– Best practices for data science and AI projects\n– Avoiding typical pitfalls in data science and AI projects\n– Discussion about the status quo and the future of data science and AI\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Interdisciplinary Qualifications 2 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807401<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
\n
Ethics, Compliance and Data Protection<\/h5>\n
\n
\n
2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
\n
1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Ethics\n– Basic ethical concepts and problems\n– Ethical consideration of big data and artificial intelligence\n– Corporate social responsibility\n– …\n\nIT compliance and IT risk management\n– IT governance and IT compliance\n– IT risks and IT risk management\n– …\n\nData protection\n– Basic concepts and overview\n– National and international legal requirements\n– General Data Protection Regulation\n– Law enforcement in the area of data protection\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Master's Thesis and Master's Examination 2 |\n Modul\/Final Examination\n (FA)\n | Coursecode:\n M250807405<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
\n
Master's Examination<\/h5>\n
\n
\n
2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
\n
0.00<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Preparation for the final Master’s examination \n– Preparation of the presentation of the key points of the written Master’s thesis according to templates and specifications of the degree program\n– Preparation for the defense of the written Master’s thesis\n– Preparation for the partial examinations on important curriculum-relevant content\n– …\n\nTaking the final Master’s examination \n– Presentation of the key points of the written Master’s thesis\n– Defense of the written Master’s thesis \n– Expert explanation and discussion of important curriculum-relevant content\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Master's Thesis and Master's Examination 2 |\n Master's Thesis\n (MA)\n | Coursecode:\n M250807404<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
\n
Master's Thesis<\/h5>\n
\n
\n
20.00<\/h5>\n ECTS<\/span>\n <\/div>\n
\n
0.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Scientific research\n– Independent implementation of the planned research project according to scientific quality criteria\n– Regular coordination with the supervisor of the Master’s thesis\n– …\n\nWriting the Master’s thesis\n– Writing the Master’s thesis according to certain norms, standards, templates and specifications of the degree program\n– Regular coordination with the supervisor of the Master’s thesis\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Master's Thesis and Master's Examination 2 |\n Seminar\n (SE)\n | Coursecode:\n M250807403<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
\n
Master's Thesis Seminar<\/h5>\n
\n
\n
2.50<\/h5>\n ECTS<\/span>\n <\/div>\n
\n
1.50<\/h5>\n SWS<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n \n Dissemination of the first results of the Master’s thesis\n– Presentation and defense of the first results of the Master’s thesis\n– Taking and reflecting on professional feedback\n– …\n\nDiscussion of various research projects\n– Discussion of initial results of other Master’s thesis projects\n– Giving professional feedback\n– …\n <\/div>\n\n <\/div>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n \n <\/div>\n <\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

To make your education as wide-ranging and practice-oriented as possible, the course content is divided into modules of different subject areas. Here you can find an overview of all modules and the associated courses.<\/p>\n","protected":false},"author":143,"featured_media":0,"parent":12,"menu_order":20,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/pages\/92"}],"collection":[{"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/users\/143"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/comments?post=92"}],"version-history":[{"count":5,"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/pages\/92\/revisions"}],"predecessor-version":[{"id":154,"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/pages\/92\/revisions\/154"}],"up":[{"embeddable":true,"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/pages\/12"}],"wp:attachment":[{"href":"https:\/\/www.fh-joanneum.at\/industrial-data-science-and-ai-based-optimisation\/master\/en\/wp-json\/wp\/v2\/media?parent=92"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}