Erasmus and Incoming Students

Course Title: Analytics and Knowledge Discovery
Course Description: Project selection and assignment, staff selection and management; goal-oriented project organiza-tion; efficient communication and information structures; motivation; account for strategic, structural, methodological and cultural differences. Knowledge of methods (classic) such as drawing up specifications and requirements, project order, project definition and kick-off, project structuring, work packages, role profiles and distribution of tasks, scheduling and resource planning, milestone defini-tion, financing and results controlling, risk planning and design, project marketing, knowledge management. Differentiation and differentiated application of classic and agile project management. Practical exercises, e.g. using LEGO for SCRUM. The theoretical and practical contents of the course support the planning and implementation of Bachelor's theses 1 and 2.
Language: English
ECTS: 2
SWS:  2
Course Number/ID: ITSB5APMIL
Semester: 5

Course Title: Business English 2
Course Description: Speech comprehension as well as oral and written language competence is increased and any gaps in grammar filled by means of current topics from science and information technology. Reading competence: Reading and understanding as well as oral/written summarising and clarifying technical texts, interpreting different types of diagrams and deriving trend descriptions. Oral skills acquisition: Job interviews, simple negotiations, sales presentations. Written skills acquisition: CVs, correspondence (application cover letters, letters of reference), reports and summaries
ECTS: 2
SWS: 2
Course Number/ID: WINB3EN2UE
Semester: 3

Course Title: Cryptology
Course Description: Tasks of cryptology, mathematical basics, algorithms and protocols, types of attacks, historical methods of encryption, current symmetrical methods, public key methods, elliptical curves, crypto-graphic hashes, digital signatures, post-quantum cryptography.
Prerequisites: C++,  calculus, probability and combinatorics
Language: English
ECTS: 3
SWS: 2
Course Number/ID: ITSB5KRYIL
Semester: 5

Course Title: Software Design
Course Description: Software Design    Technical and methodological challenges of software design; strategies and methods of planning and problem solving for software solutions; software life cycle; software specification; UML as standard notation for software; light-weight, heavy-weight and agile process models; continuous testing, inte-gration and deployment of software; design patterns and their application; use and design of frame-works; CASE tool collections and toolchains; software quality; basic features of model-driven devel-opment; current trends in software design.
ECTS: 3
SWS: 2
Course Number/ID: ITSB5SWDVO
Semester: 5

Course Title: Software Design
Course Description: Software specification; working with selected UML diagram types as standard notation for software; continuous testing, integration and deployment of software; implementation of software design pat-terns; building and using a toolchain and CASE tool collections; using model-driven development approaches.
Language: English
ECTS: 2
SWS: 2
Course Number/ID: ITSB5SWDLB
Semester: 5

Course Title: WF: .NET Development for Industrial Applications
Course Description: Overview of .NET platform, .NET libraries, programme modules (assemblies); specific data types; introduction to C#, applied object orientation, properties; garbage collection, generics, event han-dling, delegates and lambda expressions, asynchronous programming, Language Integrated Query (LINQ); selected current topics of user interaction for the development of desktop applications as well as industry-related simulations.
Language: English
ECTS: 3
SWS: 2
Course Number/ID: ITSB5NETIL
Semester: 5

Course Title: WF: Data Analysis with Python
Course Description: Introduction to Python. Functions, classes and exceptions, simple I/O and the most important stand-ard modules. Python IDEs and frameworks for computation (partly cloud based), special tool-boxes (pandas, matplotlib, numpy, scipy, scikit-learn) and scripting of these, implementation of classical explorative data analysis and presentation of results, tSNE or geo-plots, presentation of signals and images. Outlook: Exporting data and graphics, crawling data from the internet, building datasets, simple GUI elements.
Prerequisites: Familiar with procedureal- and object oriented programming in any programming language.
Language: English
ECTS: 3
SWS: 2
Course Number/ID: ITSB5DAPIL
Semester: 5

Course Title: WF: IT-Security Engineering
Course Description: On completion of this course, students will have the necessary knowledge of the security threats to the infrastructure of an organisation and the basic security measures that can be taken to improve its security. Threats such as denial of service and software security are discussed in class. Security measures such as IDS, firewalls and basic encryption and hashing algorithms are introduced. Students carry out practical programming exercises in Python to get acquainted with the field of IT security engineering applied to a business case. Students will also take their first steps in using an ethical hacking Linux distribution to identify and exploit threats to vulnerable software using virtual machines.

ECTS: 3
SWS: 2
Course Number/ID: WINB5ISEIL
Semester: 5

Course Title: Analytics and Knowledge Discovery
Course Description: The Machine Learning Workflow, data import, data coding, exploratory data analysis, data cleaning, handling of missing values, feature generation, Curse of Dimensionality, Kernel Density Estimators, multivariate normal distribution, Gaussian Mixture Models, PCA, t-SNE, K-means, Hierarchical Clustering. Spectral Clustering, Distances and Similarity Measures
Prerequisites: 1. Object Oriented programming competences in any language;  2. Python and basic libraries (matplotlib, numpy, pandas);  3. Mathematics & basics in probability calculus
Language: English
ECTS: 3
SWS:  2
Course Number/ID: M1AKDIL
Semester: 1

Course Title: Data Science
Course Description: Definition of Terminology in Data Science and Artificial Intelligence, Design Cycle, Extended Design Cycle, Sampling, Pre-processing, Normalization, Performance Measures, Cross Validation, Training Policies, K-nearest Neighbour and Minimum Distance Classifier, NLP Pre-processing and Features, Low Level Image Features
Prerequisites: 1. Object Oriented programming competences in any language;  2. Python and basic libraries (matplotlib, numpy, pandas);  3. Mathematics & basics in probability calculus
Language: English
ECTS: 5
SWS: 3
Course Number/ID: M1DSCIL
Semester: 1

Course Title: Digital Signal Processing 1
Course Description: Theory of discrete signals and systems, discrete Fourier transformation, FFT, power density spectrum, discrete convolution and correlation, interpolation, calculations in z-domain, z-transfer func-tion, stability and frequency response of discrete systems, discretization of continuous systems (bilinear transformation, impulse invariant transformation), digital filters, principle and design of FIR filters, principle and design of IIR filters, IIR filter structures, quantization problems frequency transformations, simulation of signal processing algorithms and implementation of discrete systems in lab environment (e.g. Matlab, Python, C)
Language: English
ECTS: 5
SWS: 3
Course Number/ID: M1DSCIL
Semester: 1

Course Title: Discussion & Argumentation Skills
Course Description: Argumentation, negotiation and discussion techniques, use of appropriate phrases and rhetorical devices, practical examples and role plays.
ECTS: 2
SWS: 1
Course Number/ID: M1DASIL
Semester: 1

Course Title: Mathematics & Modelling
Course Description: Vector valued functions on n-dimensional domains, vector fields, scalar fields, partial derivatives, gradient operator, Jacobi and Hessian matrix, directional derivative, Taylor series in several variables, critical points, local minima, maxima and saddle points, convex optimization and applications. Integral calculus, Pre-Hilbert (inner-product) space, (orthonormal-) basis and basis transformation, Eigenvalues, Eigenvectors, matrix decompositions and applications (PCA).
Prerequisites: one-dimensional calculus, basic linear algebra and vector calculus
Language: English
ECTS: 5
SWS: 4
Course Number/ID: AISM1MAMIL
Semester: 1

Course Title: New Business Models
Course Description: New business models (especially with regard to the circular and sharing economy), terminology in the context of digitalisation (digital business, products, processes), trends in the digital economy, metrics for monitoring new business models, implementation in modern software systems
Language: English
ECTS: 5
SWS: 3
Course Number/ID: BINM1NBMIL
Semester: 1

Course Title: Software & Process Notations
Course Description: Textual and graphical notations for software development and process modeling (e.g. BPMN, SPEM); notations for service and interface specifications; use of common notation tools; use of domain-specific UML profiles; meta-modeling; current topics in software notations.
Language: English
ECTS: 3
SWS: 2
Course Number/ID: M1SPNIL
Semester: 1

Course Title: Agile Project Management
Course Description: The focus is on the creation of software engineering projects to cope with the digitalization of companies. Project management and software engineering skills are to be applied in the practical implementation. Among other things, business case & product innovation (using business canvas & value proposition canvas), project organization (process-oriented and agile procedure models, roles, work packages, milestones, reporting, results). The project implementation is carried out with templates from Software Engineering for the development, documentation and communication of software architectures using ARC42 (Context, Requirements, Constraints, Concept of Operations, Major building blocks/components, Block diagram, interfaces, workflow, control flow).
Language: English
ECTS: 3
SWS:  2
Course Number/ID: AISM3APMIL
Semester: 3

Course Title: Big Data & Cloud Computing
Course Description: Paradigms and characteristics of Big Data and cloud computing; Overview of common Big Data frameworks and business-relevant cloud infrastructures; Programming techniques for data-intensive applications and use of hybrid cloud-based infrastructures for data-intensive software development including edge computing; Implementation of case studies; Selected chapters from Big Data Computing.
Language: English
ECTS: 3
SWS: 2
Course Number/ID: BINM3BDCIL
Semester: 3

Course Title: Business Analytics & Financial Modelling
Course Description: The course addresses the effects of digitalisation on controlling and financial management. It shows the requirements and challenges arising from the digital transformation and at the same time illustrates the new analysis possibilities. The subject of the course is the analysis of business models and companies as well as their mapping, planning and evaluation within the framework of a financial model. Current and practice-relevant methods, instruments and programmes are used, which are predominant for the evaluation and preparation of past financial information as well as for the prognosis of future planning calculations. The creation of a financial model based on this is carried out in consideration of existing modelling guidelines and best practices and takes appropriate account of the need for sensitivity and scenario analyses.
Language: English
ECTS: 3
SWS: 2
Course Number/ID: BINM3BAFIL
Semester: 3

Course Title: Business Process Management
Course Description: Business Process Management (BPM), Change- und Transformation Management, Agile Management, Digital Process Management, Agile Management and Agile Development, Metrics for Digital Process Management
Language: English
ECTS: 4
SWS: 3
Course Number/ID: BINM3BPMIL
Semester: 3

Course Title: Digital Customer Management
Course Description: In this course, students learn about typical problems of strategic and digitalised customer management and their solution approaches. For this purpose, basic methods and digital concepts (e.g. digital customer acquisition, digital cross-selling, customer retention, complaint management in the context of digitalisation) and their implementation in practice are discussed. Specifically, the following topics will be discussed:
 - Basic understanding of the CRM approach as well as market-oriented & increasingly digital customer management.
 - Understanding of the difference between past-related customer evaluation and predicted customer evaluation as well as their respective strengths and weaknesses.
 - Knowledge of key customer management strategies and metrics
 - Understand the specific challenges of implementing value-based customer management and appropriate approaches to address them.
 - Critical examination of typical customer management scenarios
 - Deriving customer management strategies on the basis of the key variables customer lifetime value and customer equity
 - Evaluation of alternative actions in digitalised customer management
Language: English
ECTS: 3
SWS: 2
Course Number/ID: BINM3DCMIL
Semester: 3

Course Title: EC: Applied Natural Language Processing
Course Description: Methods: Dialog-based Agents and Systems; Artificial Intelligence; task-oriented dialog systems and chatbots; Natural Language Generation, Interaction and Understanding; Question Answering, Slot Filling. Applications: Dialog systems and chatbots. Tools: Python, scikit-learn, nltk, ten-sorflow/keras/PyTorch, dialogflow.
Prerequisites: only in combination with EC Natural Language Processing      
AIS data science lectures of term 1 and 2 https://www.fh-salzburg.ac.at/studium/it/applied-image-and-signal-processing-joint-master/curriculum
Language: English
ECTS: 2
SWS: 1
Course Number/ID: AISM3ANLIL
Semester: 3

Course Title: EC: Applied Reinforcement Learning
Course Description: Deep RL, Reinforcement Learning Algorithms, Model-based RL, RL by use of Physics Engines
Prerequisites: only in combination with EC Reinforcement Learning
AIS data science lectures of term 1 and 2 https://www.fh-salzburg.ac.at/studium/it/applied-image-and-signal-processing-joint-master/curriculum
Language: English
ECTS: 3
SWS: 2
Course Number/ID: AISM3ARLIL
Semester: 3

Course Title: EC: Natural Language Processing
Course Description: Methods: Natural Language Processing with Deep Neural Networks, e.g. Recurrent Neuronal Networks, Attention-Models, Transformers or BERT. Contextualized representations, Subword tokenization, Beam Search.
Prerequisites: AIS data science lectures of term 1 and 2 https://www.fh-salzburg.ac.at/studium/it/applied-image-and-signal-processing-joint-master/curriculum
Language: English
ECTS: 3
SWS: 2
Course Number/ID: AISM3RILIL
Semester: 3

Course Title: EC: Reinforcement Learning
Course Description: Markov Decision Process, Definition of RL, Components of RL (Agent, Policy, Model), Model and Non-model based RL, Optimization of RL, Deep RL, Reinforcement Learning Algorithms
Prerequisites:    AIS data science lectures of term 1 and 2 https://www.fh-salzburg.ac.at/studium/it/applied-image-and-signal-processing-joint-master/curriculum
Language: English
ECTS: 3
SWS: 2
Course Number/ID: AISM3RILIL
Semester: 3

Course Title: Ethics & Sustainability
Course Description: Overview of: bioethics, medical ethics, animal ethics, ethics and politics, ethics and economy, individual ethics, and environmental ethics. This symposium examines terminology and introduces participants to concepts of professional ethics and sustainability.
ECTS: 1
SWS: 1
Course Number/ID: M3ESAIL
Semester: 3

Course Title: Intercultural Communication Skills
Course Description:
ECTS: 2
SWS: 1,5
Course Number/ID: M3ICSIL
Semester: 3

Course Title: Numerics & Industrial Algorithms
Course Description: Numerical error calculus and number representation, methods of numerical mathematics (e.g. in the field of linear algebra, approximation theory or analysis), algorithms and data structures in the application area of industrial informatics (e.g. geometric-numerical calculation, convex hull, domain search, triangulations).
Language: English
ECTS: 3
SWS: 2
Course Number/ID: IIRM3NIAIL
Semester: 3

Course Title: SP: Deep Learning
Course Description: Deep Learning Paradigm, Representation Learning, Convolutional Neural Networks, Fully Convolutional Networks, Generative Adversarial Networks, Skip Connections, Parameterisation and Model Selection/Design. Application areas: Image classification, Object detection, Image segmentation. Tools: Python, Pytorch/Tensor-Flow, Anaconda, Git, Unix/Bash, GPUs. Other aspects: Optimal use of hardware (GPUs, GPU clusters) and software resources.
Prerequisites: AIS data science lectures of term 1 and 2 https://www.fh-salzburg.ac.at/studium/it/applied-image-and-signal-processing-joint-master/curriculum
Language: English
ECTS: 5
SWS: 3
Course Number/ID: BINM3DLGIL
Semester: 3