{"id":75867,"date":"2025-11-13T12:43:09","date_gmt":"2025-11-13T12:43:09","guid":{"rendered":"https:\/\/content.technikum-wien.at\/?page_id=75867"},"modified":"2025-11-17T09:51:31","modified_gmt":"2025-11-17T09:51:31","slug":"curriculum-master-artificial-intelligence-data-science","status":"publish","type":"page","link":"https:\/\/content.technikum-wien.at\/en\/curriculum-master-artificial-intelligence-data-science\/","title":{"rendered":"Curriculum: Master Artificial Intelligence & Data Science"},"content":{"rendered":"\n
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Curriculum: Master Artificial Intelligence & Data Science<\/h1>\n<\/div>\n<\/div>\n\n\n\n
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Master<\/a><\/div>\n\n\n\n
Artificial Intelligence & Data Science<\/a><\/div>\n\n\n\n
Curriculum<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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1st Semester<\/h2>\n<\/div>\n<\/div>\n\n\n\n
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Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

    \n
  1. Self-implement baseline supervised learners (KNN, decision trees, Naive Bayes, linear regression) from problem formulation to prediction. <\/li>\n\n\n\n
  2. Apply data preprocessing and feature engineering (e.g. normalization, one-hot encoding, handling missing data, outliers, class imbalance, feature selection) for impact on model behavior. <\/li>\n\n\n\n
  3. Evaluate model performance and validation using appropriate metrics, train\/validation\/test protocols, and bias\u2013variance considerations to diagnose overfitting and generalization. <\/li>\n\n\n\n
  4. Optimize model hyperparameters to improve generalization while preventing overfitting. <\/li>\n\n\n\n
  5. Apply exploratory data analysis (EDA) to reveal structure and relationships that inform modeling choices. <\/li>\n\n\n\n
  6. Self-Implement unsupervised clustering methods (k-means, hierarchical) and assess cluster quality with validation criteria. <\/li>\n\n\n\n
  7. Self-Implement an artificial neural networks with forward\/backward propagation, activation functions, and regularization.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
    \n

    Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

      \n
    1. Design data engineering pipelines addressing the complete lifecycle from acquisition to visualization. <\/li>\n\n\n\n
    2. Analyze stakeholder requirements to define appropriate data structures, storage models, and delivery formats. <\/li>\n\n\n\n
    3. Implement data wrangling and transformation processes using Python (e.g. pandas) to prepare diverse datasets for analysis. <\/li>\n\n\n\n
    4. Develop interactive data visualizations and statistical summaries for exploratory data analysis. <\/li>\n\n\n\n
    5. Apply database technologies (SQL, NoSQL, GraphDB) and data integration techniques to manage heterogeneous data sources. <\/li>\n\n\n\n
    6. Evaluate data architectures including warehouses, lakes, and streaming systems in terms of scalability and real-time processing needs. <\/li>\n\n\n\n
    7. Extract unstructured data (text, images, audio, video, web data) using appropriate APIs and scraping techniques for analytical use.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
      \n

      Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

        \n
      1. Apply version control principles using Git and collaborative workflows for machine learning projects. <\/li>\n\n\n\n
      2. Develop scalable ML applications using APIs (REST, JSON, gRPC) and modern frameworks such as FastAPI. <\/li>\n\n\n\n
      3. Implement containerized environments with Docker and Docker Compose to ensure portability and reproducibility. <\/li>\n\n\n\n
      4. Design microservice architectures that integrate ML components, data pipelines, and distributed computing frameworks. <\/li>\n\n\n\n
      5. Automate deployment and integration pipelines using CI\/CD tools such as GitHub Actions. <\/li>\n\n\n\n
      6. Monitor ML systems through logging, alerting, and performance metrics to maintain operational reliability. <\/li>\n\n\n\n
      7. Test machine learning components using unit and integration testing to validate functionality and robustness.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
        \n

        Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

          \n
        1. Apply and evaluate statistical regression models including linear, logistic, polynomial, and regularized methods (Ridge, Lasso) for predictive and inferential analysis. <\/li>\n\n\n\n
        2. Evaluate time series models (e.g. linear regression, Holt\u2013Winters, ARIMA) for trend, seasonality, and forecast accuracy. <\/li>\n\n\n\n
        3. Apply dimensionality reduction techniques such as PCA and multidimensional scaling to uncover latent data structure. <\/li>\n\n\n\n
        4. Apply probabilistic and clustering models (Gaussian mixtures) for unsupervised data analysis and anomaly detection. <\/li>\n\n\n\n
        5. Design classical hypothesis testing procedures (t-test, ANOVA, chi\u00b2, rank-sum) to assess relationships and differences in data. <\/li>\n\n\n\n
        6. Compute effect sizes, confidence intervals, and power to support rigorous statistical inference. <\/li>\n\n\n\n
        7. Design procedures for multiple hypothesis correction to control false discovery rates in complex experiments.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
          \n

          Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

            \n
          1. Analyze and evaluate business model innovations and their impact on competitive advantage and market dynamics. <\/li>\n\n\n\n
          2. Develop innovation use cases that align technological opportunities with customer and stakeholder needs. <\/li>\n\n\n\n
          3. Design and evaluate a complete business case integrating market analysis, financial viability, and implementation strategy. <\/li>\n\n\n\n
          4. Evaluate the innovation process from ideation to commercialization, emphasizing creativity and feasibility.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
            \n

            Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

              \n
            1. Analyze stakeholder needs and system contexts to define AI project boundaries and success criteria. <\/li>\n\n\n\n
            2. Model AI system behaviors and scenarios using structured approaches such as use-case and probabilistic modeling. <\/li>\n\n\n\n
            3. Prioritize requirements and trade-offs considering uncertainty, performance, and resource constraints. <\/li>\n\n\n\n
            4. Evaluate requirements traceability, verification, and validation to maintain consistency across the AI development lifecycle.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
              \n

              Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                \n
              1. Identify and explain the tasks and instruments of leadership (for example, delegation, agreement on objectives) as well as to explain classical management models (for example, the leading continuum, the maturity model), to weigh the advantages and disadvantages of different leadership theories and concepts and to apply these to practical examples. <\/li>\n\n\n\n
              2. Reflect on and manage cultural stereotypes, prejudices and differences. <\/li>\n\n\n\n
              3. Work effectively in intercultural teams as participants and leaders to overcome and resolve cultural problems and issues. <\/li>\n\n\n\n
              4. Understand the essential techniques of leadership in an intercultural context. <\/li>\n\n\n\n
              5. Apply and evaluate methods for overcoming cultural differences in communication and decision-making. <\/li>\n\n\n\n
              6. Facilitate intercultural team training activities.<\/li>\n<\/ol>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n
                \n
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                2nd Semester<\/h2>\n<\/div>\n<\/div>\n\n\n\n
                \n
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                Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                  \n
                1. Apply advanced neural network architectures such as CNNs, RNNs, LSTMs, GANs, and Transformers. <\/li>\n\n\n\n
                2. Analyze architectural trade-offs across deep learning models in terms of representational power, efficiency, and applicability to different data modalities. <\/li>\n\n\n\n
                3. Evaluate model performance and robustness using advanced evaluation metrics and diagnostic tools for deep and ensemble learning systems. <\/li>\n\n\n\n
                4. Apply unsupervised learning techniques (DBSCAN, UMAP) for clustering and dimensionality reduction in complex datasets. <\/li>\n\n\n\n
                5. Apply ensemble learning strategies including bagging, boosting, and random forests to improve model generalization. <\/li>\n\n\n\n
                6. Optimize training efficiency through parameter tuning, gradient management, and regularization techniques in deep neural networks. <\/li>\n\n\n\n
                7. Compare and understand a wide variety of machine learning methods to select appropriate algorithms (e.g. from trees over SVMs to ANNs) for given problem contexts.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                  \n

                  Natural Language Processing AI<\/strong><\/p>\n\n\n\n

                  <\/p>\n\n\n\n

                  Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                    \n
                  1. Analyze language data and corpora to identify structure, meaning, and context in text-based information. <\/li>\n\n\n\n
                  2. Implement machine learning pipelines for text processing, including feature extraction and embeddings. <\/li>\n\n\n\n
                  3. Self-implement a basic transformer version to deeply understand AI-based text generation <\/li>\n\n\n\n
                  4. Apply neural network architectures such as seq2seq, GRU, and transformers for natural language understanding and generation. <\/li>\n\n\n\n
                  5. Evaluate large language models and knowledge graph integration for reasoning and information retrieval. <\/li>\n\n\n\n
                  6. Design topic modeling and semantic graph structures to uncover hidden relationships in textual datasets. <\/li>\n\n\n\n
                  7. Assess bias and ethical issues in embeddings and language models to promote fairness and interpretability. <\/li>\n\n\n\n
                  8. Develop applications leveraging NLP and graph-based learning for real-world problem-solving.<\/li>\n<\/ol>\n\n\n\n

                    <\/p>\n\n\n\n

                    Evolutionary and Logic-Based AI<\/strong><\/p>\n\n\n\n

                    <\/p>\n\n\n\n

                    Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                      \n
                    1. Explain knowledge representation principles and their logical underpinnings for intelligent systems. <\/li>\n\n\n\n
                    2. Apply formal reasoning techniques using logical semantics and knowledge-based frameworks. <\/li>\n\n\n\n
                    3. Implement evolutionary algorithms for optimization tasks. <\/li>\n\n\n\n
                    4. Analyze parameter control and fitness evaluation mechanisms in evolutionary computation. <\/li>\n\n\n\n
                    5. Integrate logical reasoning and evolutionary models to address complex decision-making problems. <\/li>\n\n\n\n
                    6. Evaluate strengths and limitations of symbolic and evolutionary AI paradigms across application contexts.<\/li>\n<\/ol>\n\n\n\n

                      <\/p>\n\n\n\n

                      Data Warehouse & Business Intelligence<\/strong><\/p>\n\n\n\n

                      <\/p>\n\n\n\n

                      Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                        \n
                      1. Design conceptual and logical data warehouse models using dimensional modeling and star schemas. <\/li>\n\n\n\n
                      2. Implement ETL processes to extract, transform, and load data for business intelligence systems. <\/li>\n\n\n\n
                      3. Apply BI techniques such as OLAP, visualization, and dashboards for decision support. <\/li>\n\n\n\n
                      4. Evaluate data warehouse architectures and technologies for scalability, performance, and integration. <\/li>\n\n\n\n
                      5. Develop customer analytics and business applications to derive insights from organizational data. <\/li>\n\n\n\n
                      6. Utilize modern BI tools (e.g., Power BI) for reporting and performance monitoring. <\/li>\n\n\n\n
                      7. Integrate concepts of data governance and business strategy into BI implementation planning.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                        \n

                        Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                          \n
                        1. Define real-world AI problem statements, scope, and success criteria grounded in stakeholder needs and constraints. <\/li>\n\n\n\n
                        2. Plan targeted research and skill-acquisition roadmaps to address prior-knowledge gaps and project prerequisites. <\/li>\n\n\n\n
                        3. Formulate testable hypotheses and experimental designs to compare viable solution alternatives. <\/li>\n\n\n\n
                        4. Implement end-to-end prototypes and data\/ML pipelines leveraging HPC\/cloud resources where appropriate. <\/li>\n\n\n\n
                        5. Evaluate model performance and system behavior using rigorous metrics, baselines, and reproducible validation. <\/li>\n\n\n\n
                        6. Iterate solution alternatives by analyzing limitations, risks, and trade-offs to improve technical and practical viability. <\/li>\n\n\n\n
                        7. Communicate project methods, results, and documentation through structured reports, presentations, and progress checks.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                          \n

                          Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                            \n
                          1. Define real-world AI problem statements, scope, and success criteria grounded in stakeholder needs and constraints. <\/li>\n\n\n\n
                          2. Plan targeted research and skill-acquisition roadmaps to address prior-knowledge gaps and project prerequisites. <\/li>\n\n\n\n
                          3. Formulate testable hypotheses and experimental designs to compare viable solution alternatives. <\/li>\n\n\n\n
                          4. Implement end-to-end prototypes and data\/ML pipelines leveraging HPC\/cloud resources where appropriate. <\/li>\n\n\n\n
                          5. Evaluate model performance and system behavior using rigorous metrics, baselines, and reproducible validation. <\/li>\n\n\n\n
                          6. Iterate solution alternatives by analyzing limitations, risks, and trade-offs to improve technical and practical viability. <\/li>\n\n\n\n
                          7. Communicate project methods, results, and documentation through structured reports, presentations, and progress checks.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                            \n

                            Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                              \n
                            1. Analyze scientific papers critically. <\/li>\n\n\n\n
                            2. Evaluate research quality and originality through peer discussion and comparative review. <\/li>\n\n\n\n
                            3. Apply academic writing conventions to structure and compose a concise scientific manuscript. <\/li>\n\n\n\n
                            4. Present scientific work orally and in writing using appropriate terminology, rigor, and academic style. <\/li>\n\n\n\n
                            5. Formulate a research proposal that defines objectives, methods, and expected contributions for a master\u2019s thesis.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                              \n

                              Computer Vision AI<\/strong><\/p>\n\n\n\n

                              <\/p>\n\n\n\n

                              Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                \n
                              1. Apply deep learning architectures (CNNs, Vision Transformers) for image understanding and recognition. <\/li>\n\n\n\n
                              2. Apply traditional and modern feature extraction techniques for image analysis and object detection. <\/li>\n\n\n\n
                              3. Self-implement a basic generative image model to understand modern AI-based image generations <\/li>\n\n\n\n
                              4. Design computer vision pipelines integrating e.g. segmentation and metric learning. <\/li>\n\n\n\n
                              5. Analyze the physical and geometric principles of vision, including light, reflection, and 3D reconstruction. <\/li>\n\n\n\n
                              6. Evaluate model robustness and interpretability under adversarial conditions and explainability constraints. <\/li>\n\n\n\n
                              7. Develop innovative vision-based applications through project work and critical review of research literature.<\/li>\n<\/ol>\n\n\n\n

                                <\/p>\n\n\n\n

                                Big Data Analytics<\/strong><\/p>\n\n\n\n

                                <\/p>\n\n\n\n

                                Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                  \n
                                1. Design data processing architectures for large-scale, distributed, and real-time analytics. <\/li>\n\n\n\n
                                2. Implement batch and streaming pipelines using Spark, MapReduce, and\/or Kafka for scalable computation. <\/li>\n\n\n\n
                                3. Apply machine learning techniques in distributed environments to analyze big data efficiently. <\/li>\n\n\n\n
                                4. Integrate monitoring and visualization tools (Grafana, ClickView) to interpret system performance and analytical results. <\/li>\n\n\n\n
                                5. Develop automated data workflows with orchestration tools like Dagster for continuous analytics. <\/li>\n\n\n\n
                                6. Evaluate scalability, fault tolerance, and efficiency in large-scale learning and streaming systems.<\/li>\n<\/ol>\n\n\n\n

                                  <\/p>\n\n\n\n

                                  Predictive Maintenance<\/strong><\/p>\n\n\n\n

                                  <\/p>\n\n\n\n

                                  Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                    \n
                                  1. Analyze reliability and survival data to identify patterns of system degradation and failure. <\/li>\n\n\n\n
                                  2. Apply statistical models and estimators (e.g., Kaplan\u2013Meier, hazard rate, exponential models) for failure prediction. <\/li>\n\n\n\n
                                  3. Develop parametric and proportional hazards models to describe and compare reliability behavior. <\/li>\n\n\n\n
                                  4. Implement machine learning and deep learning methods for survival and reliability prediction. <\/li>\n\n\n\n
                                  5. Evaluate model performance and interpret results using empirical and case-based validation. <\/li>\n\n\n\n
                                  6. Integrate predictive maintenance strategies into industrial and data-driven contexts through applied project work.<\/li>\n<\/ol>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n
                                    \n
                                    \n

                                    3rd Semester<\/h2>\n<\/div>\n<\/div>\n\n\n\n
                                    \n
                                    \n
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                                    Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                      \n
                                    1. Formulate a thesis proposal that demonstrates originality, feasibility, and scientific contribution. <\/li>\n\n\n\n
                                    2. Plan a structured research process with clear milestones, supervision interactions, and documentation checkpoints. <\/li>\n\n\n\n
                                    3. Develop a functional prototype or experimental implementation that supports the research objectives. <\/li>\n\n\n\n
                                    4. Evaluate results and findings using rigorous academic and methodological standards. <\/li>\n\n\n\n
                                    5. Present and defend a first working prototype and first research results to panel of experts.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                                      \n

                                      Machine Learning Engineering<\/strong><\/p>\n\n\n\n

                                      <\/p>\n\n\n\n

                                      Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                        \n
                                      1. Implement modern machine learning architectures and foundational models, including fine-tuning and model integration strategies. <\/li>\n\n\n\n
                                      2. Apply parameter-efficient optimization methods such as LoRA, QLoRA, and model compression for performance scalability. <\/li>\n\n\n\n
                                      3. Develop model serving and deployment pipelines using distributed frameworks and inference optimizations. <\/li>\n\n\n\n
                                      4. Analyze model performance trade-offs through benchmarking, A\/B testing, and profiling. <\/li>\n\n\n\n
                                      5. Optimize training and inference efficiency using hardware-aware techniques, quantization, and mixed-precision computation. <\/li>\n\n\n\n
                                      6. Evaluate distillation and optimization strategies to balance performance, efficiency, and generalization. <\/li>\n\n\n\n
                                      7. Design production-ready ML systems integrating distributed training, efficient inference, and deployment best practices.<\/li>\n<\/ol>\n\n\n\n

                                        <\/p>\n\n\n\n

                                        Autonomous AI Systems & RFL<\/strong><\/p>\n\n\n\n

                                        <\/p>\n\n\n\n

                                        Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                          \n
                                        1. (Self)-Implement reinforcement learning algorithms including bandit, SARSA, and Q-learning approaches. <\/li>\n\n\n\n
                                        2. Model decision-making processes using Markov Decision Processes and value-based learning methods. <\/li>\n\n\n\n
                                        3. Apply deep reinforcement learning techniques such as Deep Q-Networks and policy gradients to complex tasks. <\/li>\n\n\n\n
                                        4. Evaluate agent performance and learning dynamics through experimentation and comparative analysis. <\/li>\n\n\n\n
                                        5. Design autonomous systems integrating reinforcement learning with adaptive and interactive behavior. <\/li>\n\n\n\n
                                        6. Collaborate in project-based research applying RL methods to real-world environments and competitive simulations.<\/li>\n<\/ol>\n\n\n\n

                                          <\/p>\n\n\n\n

                                          Data Science with R<\/strong><\/p>\n\n\n\n

                                          <\/p>\n\n\n\n

                                          Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                            \n
                                          1. Apply the R programming language for data analysis, manipulation, and visualization. <\/li>\n\n\n\n
                                          2. Implement data engineering workflows using the tidyverse ecosystem for structured data processing. <\/li>\n\n\n\n
                                          3. Develop visual analytics with ggplot2 for data exploration and communication. <\/li>\n\n\n\n
                                          4. Construct and evaluate predictive and statistical models using the tidymodels framework. <\/li>\n\n\n\n
                                          5. Deploy interactive data applications through R Shiny for practical and web-based insights. <\/li>\n\n\n\n
                                          6. Manage data-driven projects from concept to presentation, demonstrating analytical and programming proficiency.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                                            \n

                                            Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                            <\/p>\n\n\n\n

                                              \n
                                            1. Analyze current advancements in AI and data science across models, algorithms, and applications. <\/li>\n\n\n\n
                                            2. Evaluate emerging technologies and research trends shaping the future of AI systems. <\/li>\n\n\n\n
                                            3. Discuss societal, technical, and ethical challenges associated with cutting-edge developments in AI. <\/li>\n\n\n\n
                                            4. Synthesize knowledge from diverse AI domains to identify open problems and research opportunities. <\/li>\n\n\n\n
                                            5. Reflect critically on innovation directions in the context of long-term scientific and industrial impact.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                                              \n

                                              Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                \n
                                              1. Interpret the legal foundations, data protection and privacy regulations relevant to AI <\/li>\n\n\n\n
                                              2. Evaluate legal implications of handling sensitive data and fundamental rights. <\/li>\n\n\n\n
                                              3. Apply intellectual property and data ownership principles to real-world AI applications. <\/li>\n\n\n\n
                                              4. Assess cybersecurity, safety, and compliance frameworks shaping AI and data law enforcement.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                                                \n

                                                Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                  \n
                                                1. Assess ethical principles, dilemmas and theories underlying decision-making in AI systems. <\/li>\n\n\n\n
                                                2. Evaluate value alignment and human-centered design approaches to ensure responsible AI. <\/li>\n\n\n\n
                                                3. Assess the social and cultural impacts of AI on individuals and communities. <\/li>\n\n\n\n
                                                4. Discuss global ethical challenges and long-term implications of AI on society.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                                                  \n

                                                  Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                  <\/p>\n\n\n\n

                                                    \n
                                                  1. Design end-to-end machine learning operations pipelines for model deployment, monitoring, and lifecycle management. <\/li>\n\n\n\n
                                                  2. Implement model serving architectures using frameworks such as FastAPI, TensorFlow Serving, and Triton. <\/li>\n\n\n\n
                                                  3. Apply cloud and on-premise infrastructure solutions for scalable AI model hosting. <\/li>\n\n\n\n
                                                  4. Develop AI agent and LLM-based systems integrating retrieval-augmented generation and multi-agent coordination. <\/li>\n\n\n\n
                                                  5. Use experiment tracking and monitoring tools (WandB, Langfuse) to manage model performance and operational health. <\/li>\n\n\n\n
                                                  6. Manage model and data versioning workflows using DVC and reproducibility best practices. <\/li>\n\n\n\n
                                                  7. Evaluate model performance, drift, and reliability using automated testing and debugging methodologies. 8. Integrate MLOps frameworks into production environments with emphasis on continuous delivery and maintainability.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                                                    \n

                                                    Robotics AI<\/strong><\/p>\n\n\n\n

                                                    <\/p>\n\n\n\n

                                                    Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                    <\/p>\n\n\n\n

                                                      \n
                                                    1. Explain the fundamental principles of industrial and mobile robotics, including kinematics, control, and automation. <\/li>\n\n\n\n
                                                    2. Operate robotic simulation and programming tools for industrial tasks. <\/li>\n\n\n\n
                                                    3. Implement vision-based and geometric path-following algorithms for 2D and 3D object interaction. <\/li>\n\n\n\n
                                                    4. Develop robotic workflows for pick-and-place and autonomous navigation in digital factory environments. <\/li>\n\n\n\n
                                                    5. Apply probabilistic robotics concepts for perception, localization, and decision-making. <\/li>\n\n\n\n
                                                    6. Evaluate robotic systems performance and safety through practical experimentation and testing.<\/li>\n<\/ol>\n\n\n\n

                                                      <\/p>\n\n\n\n

                                                      Explainable and Trustworthy AI \/ AI Security<\/strong><\/p>\n\n\n\n

                                                      <\/p>\n\n\n\n

                                                      Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                        \n
                                                      1. Evaluate the motivations and foundations of explainable and trustworthy AI in real-world contexts. <\/li>\n\n\n\n
                                                      2. Apply interpretability techniques such as global, local, and partial dependence analyses. <\/li>\n\n\n\n
                                                      3. Implement model-agnostic explainability methods including LIME, SHAP, and counterfactual reasoning. <\/li>\n\n\n\n
                                                      4. Evaluate fairness, bias, and transparency metrics in AI systems. <\/li>\n\n\n\n
                                                      5. Analyze robustness, uncertainty, and adversarial vulnerabilities in machine learning models. <\/li>\n\n\n\n
                                                      6. Apply privacy-preserving and governance techniques to ensure secure and compliant AI operation. <\/li>\n\n\n\n
                                                      7. Reflect on future trends and ethical considerations surrounding AI security and trustworthiness.<\/li>\n<\/ol>\n\n\n\n

                                                        AI-driven Marketing<\/strong><\/p>\n\n\n\n

                                                        <\/p>\n\n\n\n

                                                        Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                        <\/p>\n\n\n\n

                                                          \n
                                                        1. Explain AI applications and data-driven strategies in modern marketing ecosystems. <\/li>\n\n\n\n
                                                        2. Apply machine learning models for customer segmentation, targeting, and personalization. <\/li>\n\n\n\n
                                                        3. Develop and evaluate predictive analytics pipelines for customer lifetime value and marketing performance. <\/li>\n\n\n\n
                                                        4. Design automated marketing systems using recommendation engines and chatbots. <\/li>\n\n\n\n
                                                        5. Analyze social media, sentiment, and content data using NLP and computer vision methods. <\/li>\n\n\n\n
                                                        6. Evaluate AI ethics, bias, and transparency in digital marketing practices. <\/li>\n\n\n\n
                                                        7. Integrate AI tools and platforms to optimize campaigns and strategic marketing decisions.<\/li>\n<\/ol>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n
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                                                          \n

                                                          4th Semester<\/h2>\n<\/div>\n<\/div>\n\n\n\n
                                                          \n
                                                          \n
                                                          \n

                                                          Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                          <\/p>\n\n\n\n

                                                            \n
                                                          1. Document the progression of their master thesis through structured supervision meetings. <\/li>\n\n\n\n
                                                          2. Present their research effectively through multiple formats including written, oral, and multimedia presentations. <\/li>\n\n\n\n
                                                          3. Publish scientific results in accordance with academic and ethical standards. <\/li>\n\n\n\n
                                                          4. Present and defend research findings in the academic setting of the AI master symposium.<\/li>\n<\/ol>\n<\/div><\/div>\n\n\n\n
                                                            \n

                                                            Upon successful completion of this course, students will be able to \u2026<\/p>\n\n\n\n

                                                              \n
                                                            1. Design a comprehensive research project that demonstrates mastery of advanced AI and data science concepts. <\/li>\n\n\n\n
                                                            2. Conduct independent research using appropriate methodologies, analytical tools, and ethical practices. <\/li>\n\n\n\n
                                                            3. Evaluate results critically and interpret findings in the context of current scientific and industrial developments. <\/li>\n\n\n\n
                                                            4. Compose a formal thesis document that meets academic standards for structure, clarity, and rigor. 5. Defend the research and its conclusions through a scholarly oral examination before an expert committee.<\/li>\n<\/ol>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n
                                                              \n
                                                              \n
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                                                              << Back to course-page<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

                                                              <\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":15,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"ppma_author":[2742],"class_list":["post-75867","page","type-page","status-publish","hentry"],"acf":{"featured_image":"","ignore_cache":false,"extra_search_terms":false,"search_term_blacklist":false,"exclude_from_search":false,"template":"","override_header":false,"override_footer":false},"yoast_head":"\nMaster Artificial Intelligence & Data Science: Curriculum | FHTW<\/title>\n<meta name=\"description\" content=\"Overview of \u2714 all courses \u2714 all contents of the AI Master's Degree Program at UAS Technikum Wien \u27a8 Learn more!\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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