Modules What you can expect during your studies

Databases & Big Data
You will design and develop your own database application, from the use case to the database schema, data import & transformation, data analytics and performance optimization to visualization and decision support. In the process, you will learn important aspects about database security. You will implement all aspects of the project with both SQL and NoSQL databases, learning to compare the two technologies. (6 ECTS)

Knowledge Representation & Reasoning
This module introduces students to the fundamentals of knowledge representation in AI. Propositional logic, predicate logic, description logics, and Baysian networks are taught with the goal that students understand and can apply relevant AI technologies such as logical modeling, bayesian networks, ontologies, knowledge graphs, and SAT solvers. (3 ECTS)

Algorithms & Data Structures
Core topics of algorithm & Datastructures are:  Complexity theory, O-Notation, Recursion, Stacks, Queues, Sets, Maps, Hash-Tables, Multimaps, Priority-Queues, Heaps, Binary-Search-Trees (BST, AVL, Splay), Sorting (Merge-, Quick-, Radix-Sort; Lower-Bound), Graphs (Traversing, Digraphs, DAG's, Shortes-Path-Tree, Minimal-Spanning-Tree). (3 ECTS)

Information Security
In this module, students will learn about fundamental concepts to reach the basic information security objectives. Students will learn about different types of threats and the technical and organizational measures to mitigate them. (3 ECTS)

Object Oriented Programming
A profound, yet practice-focused introduction into the basics of object-oriented programming using the example of Java. Through a host of practice exercises and examples, students are given the skills to develop simple, automatically tested programs. The application of selected design principles as well as the use of professional development tools complete the course content. (6 ECTS)

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Applied Statistics
In this module, key terms and concepts from statistics relevant to Data Science are introduced and practiced. This includes Exploratory Data Analysis, data and sampling distributions, statistical experiments and significance testing, regression and prediction, and an introduction to classification. Application from the Data Science context will be solved using the R programming language. (3 ECTS)

Discrete mathematics for data structures & algorithms
The DMATH-ALGO module introduces discrete mathematics for describing and analysing algorithms and data structures. This includes: Logic, proof theory, recursion, graph theory and complexity theory. Sets, matrices and graphs are abstract structures for managing data. Data manipulation can be described and analysed using relations and functions.
The content is taught using interactive videos. Students practise and consolidate the content in weekly tutorials. (3 ECTS)

Discrete mathematics for data encryption & data coding
The DMATH-CODE module introduces discrete mathematics for describing and analysing data encryption and data coding. This includes: Probability theory, combinatorics and number theory. When encrypting data, chance is a central element and probabilities can be used to assess the security of crypto protocols. Modular arithmetic and prime numbers are basic building blocks of modern cryptography and coding theory. The content is taught using interactive videos. Students practise and consolidate the content in weekly tutorials. (3 ECTS)

Linear Algebra
Linear systems of equations (Gaussian algorithm); matrix algebra; vector spaces; linear mappings, their properties, and their applications (e. g. in computer graphics); eigenvalue problems and singular value decomposition. All topics are applied to practical problems from computer science and implemented with a suitable programming language (like python, etc.). (3 ECTS)

Basics of Calculus
Basics of Calculus: Continuity, Limits, Convergence, Differential Quotient, Derivative, Integral, Product Rule, Quotient Rule, Chain Rule, Integration by Substitution, Integration by Parts, Applications to Graphs of Functions: Monotonous Functions, Zeros, Maxima and Minima, Inflection Points, Curvature. Optimisation Problems. Numerical Differentiation and Integration using Software. (3 ECTS)

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Natural Language Processing
Natural language processing (NLP) is a subfield of linguistics, computer science, and AI concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. We briefly cover classical NLP methods before focusing on deep learning for NLP, including recent advancements for large language models. (9 ECTS)

Reinforcement Learning
Fundamentals of reinforcement learning (RL); Markovian decision processes; representation of policy and value functions; basic RL algorithms such as dynamic programming, Monte-Carlo, temporal difference learning, SARSA and Q-learning; function approximation, policy gradient methods and deep reinforcement learning; application to agent programming. (6 ECTS)

Computer Vision & AI
Foundational methods of image processing, color perception and systems, image enhancement, linear filters, feature detection and description, object recognition, neuronal networks and deep learning for computer vision, object tracking; 3D reconstruction from stereo and multiple cameras; video analysis; image generation. (6 ECTS)

Machine Learning Operations
Machine learning operations (MLOps) is a set of techniques and best practices at the intersection of Machine Learning, DevOps, and Data Engineering. Its goal is to develop ML systems that are reliable, scalable, reproducible, and can be deployed into production with minimal manual overhead. This course also teaches best practices for training deep neural networks, as well as distributed training (single model on multiple GPUs). (3 ECTS)

Machine Learning
Fundamental techniques, models and architectures for supervised and unsupervised learning targeted to structured and unstructured data: regression and classification models, model evaluation, clustering, market basket analysis, dimensionality reduction and recommender systems. Introduction to deep learning with applications to image (convolutional neural nets (CNN) and transfer learning), time-series analysis (recurrent neural nets (RNN)), (large) language models (transformer architecture), GANs and diffusion models. Implementation of machine learning projects in Python. AIML Bachelor students must take ADML. All other students can take either ML (3 credits) or ADML (6 credits) as an elective, but not both.

Programming for Data Science
Students are taught the basic programming concepts as well as the basics of object-oriented programming in Python (the 'pythonic' way). Furthermore, the students get to know the important libraries NumPy, Matplotlib, Seaborn and Pandas. This enables them to implement various problems in Data Science and Artificial Intelligence. (6 ECTS)

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AI & Business
In 2015, Gartner coined the term «Algorithmic Business» to describe this next step in digital business and to pay tribute to the latest breakthrough developments in artificial intelligence, machine learning (e.g. Deep Learning), and data science. The focus is on the profitable combination of algorithms and business models adapted to them. The module deals with the development of business ideas in relation to data and artificial intelligence. (3 ECTS)

AI Ethics & Regulation
The developments in the field of artificial intelligence and the digital transformation lead to diverse ethical and normative questions. The module will introduce these discussions and clarify the ethical foundations so that these current debates can be understood and related to one's own work. The social and normative aspects of AI will be addressed and the most important considerations on the ethics of AI will be controversially debated. Ethics will also be applied practically within the framework of an ethical reflection on one's own project. (3 ECTS)

Robotics
Industrial Robots (6 Axis Articulated Robots) as well as Mobile and Humanoid Robots are to be programmed that they perform different tasks. Data from different sensors are evaluated and help the robots to do their jobs.
NOTE: AI/ML Bachelor Students at HSLU Informatik may visit the module AROB at Dept. T&A in Horw instead of this ROBO module. (They must do one or the other).
NOTE: T&A students (TM and TDE) in Horw may visit this ROBO module in Rotkreuz in addition to the AROB module. (6 ECTS)

Industrial Optimization
This module provides an introduction to the quantitative models and methods from the Decision Science (also known as Operations Research) domain, which are often used for decision-making in the manufacturing and service sectors. In particular, linear programming, integer linear programming, and constraint programming are discussed. Special emphasis is placed on modeling aspects and practical applications. For example, decision problems in production planning, employee scheduling, vehicle routing, and sports tournament scheduling are considered. Using various software, in particular spreadsheet, Pyomo (Python), Cbc, Gurobi and Google OR-tools, the quantitative models are implemented and solved. (6 ECTS)

Philosophy, Art & AI
This interdisciplinary course delves into the fascinating intersection of philosophy, art, and artificial intelligence, offering a unique exploration of its complexities and implications. Students will explore key themes such as philosophical approaches to AI and art, what it means to be an artist in AI era, how AI is portrayed in the media, and the implications of AI, all while gaining a deep understanding of how these concepts are intertwined and what it says about society.  Students will expand their perspectives on the philosophical nature and societal impact of AI, gaining invaluable insights into the future of this rapidly evolving field. (3 ECTS)

Introduction to AI
This module provides an introduction to artificial intelligence and teaches the technical foundations for successful work in AI projects.  It provides an insight into the history of AI, deals with the metaphor of the rational intelligent agent and introduces various agent architectures. In addition, the foundations for processing linguistic information in chatbot agents are covered and methods of neuro-psychology for studying human perception are taught. For AI project work, the basics of project work in data science and machine learning are introduced and practiced using various software tools such as Pyenv, Jupiter Notebooks, Docker, Git, VS Code. (6 ECTS)

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AI/ML Bachelor Project
Individual bachelor thesis in the context of the chosen subject area. The projects are assigned by business partners or research groups/lecturers. Engineering and practical implementation have a high priority. The bachelor thesis is always carried out as an individual piece of work.  (12 ECTS)

AI/ML Competition
In this module, students participate in national and international AI challenges and compete against other universities or organizations. Significant prize money is sometimes (but not always) at stake. Students will be supported to be ambitious and win these competitions. The challenges are, for example, competitions from Kaggle, or relevant Hackathons.  (6 ECTS)

Data Science Project I
Students will learn the typical data-science pipeline and data-science project management skills, through project based learning. The students will be able to select a project from a list of possible ideas in different areas of data science. All projects will always cover general core elements of data-science in a project setting. The course is focused on the early phases of a project: data engineering, data discovery and easy machine learning modelling. No cloud technologies or experiment tracking are covered in this course. The students will present their final results to a jury of experts in industry whose grades will count for 70% of the grade. This is one of two modules (DSPRO1 & DSPRO2) which must be taken consecutively, and over which the learning goals will be spread. (6 ECTS)

Data Science Project II
Students will learn the typical data-science pipeline and data-science project management skills, through project based learning. The students will be able to select a project from a list of possible ideas in different areas of data science. All projects will always cover general core elements of data-science in a project setting. This course focus on unstructured data, cloud technologies and requires as a deliverable a scientific report. It is expected that students works with unstructured data. The students will present their final results to a jury of experts in industry whose grades will count for 70% of the grade. This is one of two modules (DSPRO1 & DSPRO2) which must be taken consecutively, and over which the learning goals will be spread. (6 ECTS)

Data Visualization
Principles and concepts for the visual presentation of information. Design strategies for methods of presentation. Histories, theories and best practice for compelling data visualizations. Hands-on project work and case studies in applied data visualization. Independent assessment of design decisions concerning human perception and the significance of the visualization. Interactive visualizations. (3 ECTS)

Communication Skills
In this module, students systematically plan and write coherent and interesting specialist texts for the study program and for real-life business practice. Business-related text types from the field of Information Technology and scientific writing are the core features. Students analyze and classify multi-modal texts and assess them in terms of comprehensibility. (3 ECTS)

Project Management
The module teaches the fundamentals of classic and agile project management based on theory, exercises and a case study in the areas of project setup, requirements engineering, project planning, controlling and management as well as reporting and communication. (3 ECTS)

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