{"id":97,"date":"2024-10-17T12:24:02","date_gmt":"2024-10-17T10:24:02","guid":{"rendered":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/?page_id=97"},"modified":"2024-11-28T08:03:58","modified_gmt":"2024-11-28T07:03:58","slug":"curriculum","status":"publish","type":"page","link":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/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 Machine Learning and Generative AI:<\/h5>\n <\/div>\n \n
\n \n Machine Learning and Generative AI 1 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807209<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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High Performance Computing and Cloud Computing<\/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 High Performance Computing\n– Overview and definitions of terms \n– Processor architectures (CPU, GPU, TPU, …) \n– Relevant API technologies and microservices architectures\n– Vertical vs. horizontal scaling \n– …\n\nVirtualisation\n– Overview and types of virtualisation\n– Hardware virtualisation incl. orchestration\n– Container virtualisation incl. orchestration \n– …\n\nCluster frameworks\n– Hyper-converged infrastructure\n– Scalable data analysis frameworks\n– Introduction to operations and high availability\n– …\n\nCloud Computing\n– Overview, providers and definitions of terms\n– Cloud computing service models (XaaS)\n– Scalable data management\n– Architecture management and security management\n– Integration of cloud services for data analysis\n– Automation, monitoring and cost management\n– Selected services for data science and AI\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Machine Learning and Generative AI 2 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807210<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Game Theory and Gaming AI<\/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 Decision Theory\n– Preferences and utility functions\n– Decision situations with risk \n– Decision situations with uncertainty\n– …\n\nGame theory\n– Cooperative and non-cooperative games\n– Games with complete or incomplete information\n– Static vs. dynamic and repeated games\n– Solution concepts of game theory\n– …\n\nStochastic models\n– Markov decision problems\n– Monte Carlo approaches\n– Monte Carlo tree search\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Machine Learning and Generative AI 2 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807211<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Introduction to Probabilistic and Generative AI<\/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 Generative AI\n– Historical development\n– Basic idea and basic concepts\n– Status quo and challenges\n– …\n\nProbabilistic machine learning\n– Bayes’ theorem as an update rule\n– Product rule and Bayesian networks\n– Learning from distributions\n– …\n\nInference in probabilistic models:\n– Approximation approaches (Laplace approximation, variational approaches, …)\n– Markov chain Monte Carlo method\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 Machine Learning and Generative AI:<\/h5>\n <\/div>\n \n
\n \n Machine Learning and Generative AI 3 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807308<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Advanced Machine Learning Methods<\/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 Requirements and methods for Explainable AI\n– Explainable AI vs. black-box AI\n– Model-related vs. model-agnostic explainability\n– …\n\nState-of-the-art machine learning methods \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 Machine Learning and Generative AI 3 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807309<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Reinforcement Learning and MARL<\/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 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 simple agent-based models\n– …\n\nReinforcement Learning \n– Basic concepts and overview of methods\n– Temporal Difference Learning (Q-Learning, SARSA, …)\n– Selected frameworks, packages and libraries\n– …\n\nBasics of Multi-Agent Reinforcement Learning (MARL)\n– Basic concepts and fundamentals of MARL\n– Cooperation vs. competition between agents\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Machine Learning and Generative AI 4 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807310<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Advanced Artificial Neural Networks and Deep 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 Deep Learning\n– Deep learning frameworks for CPU, GPU and TPU computing\n– More complex architectures (e.g. U-Net, LSTM, ResNet, GCN, …)\n– …\n\nAreas of application for deep learning\n– Image and video analysis\n– Audio and signal analysis\n– \u2026\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Machine Learning and Generative AI 4 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807311<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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Advanced Generative AI Methods and Large Language Models<\/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 Texts and text mining \n– Basics of linguistics and encoding of texts\n– Latent Semantic Analysis and Latent Dirichlet Allocation\n– Word embeddings\n– …\n\nMethods and architectures for Generative AI\n– Variational Autoencoder\n– Autoregressive models and transformers\n– Flow-based generative models\n– Energy-based models and diffusion models \n– Generative adversarial networks\n– …\n\nAdvanced Natural Language Processing \n– Overview and areas of application\n– Large Language Models \n– Prompt Engineering\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Machine Learning and Generative AI 5 |\n Lecture\/Practical\n (IL)\n | Coursecode:\n M250807312<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
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DevOps and MLOps<\/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 Foundations of DevOps and MLOps\n– Principles of DevOps and MLOps\n– Technology stacks and tools\n– Continuous integration (CI) and continuous delivery (CD)\n– Special aspects of DevOps in cloud environments\n– Container as a Service (CaaS) \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– …\n\nOperational use of machine learning models\n– Operational provision of the models\n– Monitoring and further development of machine learning models during operation\n– Triggering model-related actions within a software application\n– …\n <\/div>\n\n <\/div>\n <\/button>\n \n
\n \n Machine Learning and Generative AI 6 |\n Project Thesis\n (PA)\n | Coursecode:\n M250807313<\/span>\n <\/div>\n Language of instruction\n English<\/small>\n
\n
Project Work in Machine Learning and Generative AI<\/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 \u201cMachine Learning and Generative AI\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":14,"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\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/pages\/97"}],"collection":[{"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/users\/143"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/comments?post=97"}],"version-history":[{"count":3,"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/pages\/97\/revisions"}],"predecessor-version":[{"id":146,"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/pages\/97\/revisions\/146"}],"up":[{"embeddable":true,"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/pages\/14"}],"wp:attachment":[{"href":"https:\/\/www.fh-joanneum.at\/machine-learning-and-generative-ai\/master\/en\/wp-json\/wp\/v2\/media?parent=97"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}