Lecturer: Janez Povh
Syllabus outline:
• Introduction to data modelling: what is data model, how to compute and validate data model;
• Sources of data: sensor data; data repositories; open data; data warehouses,…;
• Supervised learning: regression, classification (neural networks, logistic regression, support vector machines); analysis of supervised learning models (cross-validation, confusion matrix, precision, accuracy, recall); bootstrapping
• Unsupervised learning: clustering, principal component analysis; evaluating the unsupervised models (purity, normalized mutual information, Rand index,…)
• Data Mining methods: association rules, decision trees
• Recommendation systems: data model for a recommendation systems, content-based recommendations;
• Deep learning: image and speach recogniiton
• Data modelling with state of the art open source software: R, WEKA, Orange
Objectives and competences:
• the use of methodological tools, ie. implementation, coordination and organization of research, application of various quantitative research methods and techniques
• the use and combining the knowledge from different disciplines
• the ability to use information and communications technologies and data analytic tools in engineering
• ability to collect, store, analyse and interpret big data
Subject-specific competences:
• ability of collecting data and performing and sustainable management of data;
• ability of creating and validating advanced data models;
• mastering supervised and unsupervised statistical learning methods;
• mastering the key data mining methods;
• mastering at least one state-of-the- art tool for statistical modelling (R, Weka, Orange)
Intended learning outcomes:
The student will:
• understand the importance and potentials of data modelling;
• master the key statistical methods underlying the data modelling;
• learn how to use state-of-the-art software tools to perform advanced data modelling (R, Weka, Orange)
Lecturer: Janez Povh, Leon Kos
Syllabus outline:
• Introduction to the big data analysis: what is big data, where we find it, how to store it?
• Visualizations of big data: which tools and diagrams are suitable for representing big data.
• Simple big data analysis: search for similar items: near neighbour search, similarity preserving summaries of sets.
• Network and Link analysis: PageRank algorithm; Link spam; Hub and authorities;
• Data streams: the stream data models; sampling data in a stream; filtering streams; sensors data, decision rules based on sensor data;
• Supervised and unsupervised learning from big data: clustering, classification and regression analysis,
• Hadoop: what is Hadoop distributed file system, how map-reduce framework works, how do we generate and schedule data-related jobs.
• First steps in R and RHadoop – we will introduce programming language R and Hadoop libraries rmr and rhdfs. Additionally, RStudio as GUI will be introduced. Students will receive virtual machine with these software installed.
• Analysis, visualisation and statistical learning from big data using RHadoop
• Testing RHadoop on supercomputers: students will get access to a supercomputer at University of Ljubljana to perform really big data analysis
Objectives and competences:
The main objective of this course is to make the students competent to work with big data using state of the art hardware and software tools.
General competences:
• the use of methodological tools, ie. implementation, coordination and organization of research, application of various quantitative research methods and techniques
• the use and combining the knowledge from different disciplines
• the ability to use information and communications technologies and data analytic tools in engineering
• ability to collect, store, analyse and interpret big data
Subject-specific competences:
• knowledge of the specific features of big data
• knowledge of methods adjusted for the analysis of big data
• knowledge of tools for analyzing big data
• the ability to use high-performance computers to analyze big data
• mastering R and Hadoop for Big Data analysis
Intended learning outcomes:
The student will:
• understand the specificity of big data analysis compared to classical data analysis
• master the methods, designed for big data analysis with focus to the applications in engineering;
• learn how to use high performance computers and state of the art open source software (RHadoop) to retrieve, store and analyse big data
Lecturer: Denis Trček
Syllabus outline:
• Introduction.
• Key standards and organizations (ISO, ITU-T, IETF, W3C, OASIS, OMA).
• Security mechanisms, security services (principles and practical implementations of authentication, confidentiality, integrity, non-repudiation, access control, logging and alarming), public key infrastructure (time base, name space management, operational protocols), quantum computing basics (quantum key exchange).
• Authentication, authorization and accounting infrastructure (principles, examples of standardized solutions like RADIUS and Diameter).
• Security of physical and data layers (example protocols are WEP, WPA1 and WPA2).
• Security of network, transport and application layers, including internet of things and clouds (example protocols are IPSec, TLS, S/MIME, SET, XMLSec, SAML, XACML, WS-*).
• Formal methods (taxonomy of formal methods, examples like R. Rueppl’s method, logic BAN).
• Security and privacy by design (internet stvari, RFID systems) with trust management and reputation management basics in services oriented architectures.
• Secure programming (model checking).
• Risk management in IS, organizational views and human factor views (security policies, human factor modelling and simulations).
• Accreditation and auditing of IS related to security (ISO 2700X, CISSP), and standards for technical implementations of hardware and software components (Common Criteria).
• Basic legislation in the area of IS security and privacy (EU directives, national implementations).
• Comclusions.
• Addendum: Mini practical tasks covering the latest selected technological issues.
Objectives and competences:
The goal of the course is to educate students to be able to actively provide security and privacy in contemporary information systems (IS), which include internet of thins, be it as systems administrators, or developers of new solutions.
Categorized competences:
- Developing skills in critical, analytical and synthetic thinking.
- The ability to define, understand and solve creative professional challenges in computer and information science.
- The ability of professional communication in the native language as well as a foreign language.
- Compliance with security, functional, economic and environmental principles.
- The ability to understand and apply computer and information science knowledge to other technical and relevant fields (economics, organisational science, fine arts, etc).
- Practical knowledge and skills of computer hardware, software and information technology necessary for successful professional work in computer and information science.
Intended learning outcomes:
- Knowledge and understanding: Knowledge of the principles for protection of information resources, data, and privacy in a modern global information environment that includes internet of things and smart devices.
- Application: Administration of security and privacy IS solutions, and their development, including internet of things and smart structures.
- Reflection: Holistic understanding of information security and privacy.
- Transferable skills: The course is related to areas of operating systems, computer communications, and business views of IS security and privacy. Further, the acquired skills are also aimed at the development of new products and servivces.
Lecturer: Herakovič Niko
Syllabus outline:
Lectures – main topics:
- Overview and the role of assembly and handling systems and processes (AaHSP) in the production process.
- The reasons and conditions for AaHSP automation, basic concepts and strategies AaHSP automation.
- Economic aspects of AaHSP automation.
- Concepts of smart manual assembly and handling processes and systems. Concepts of rigid and flexible automated AaHSP. Analysis of real cases of assembly and handling processes and systems. Product and process structure.
- Planning of AaHSP. Integral approach. Relation to the product and its structure. Design for ease of assembly and handling and methods.
- Reliability and Availability of AaHSP.
- AaHSP in the factory of the future. Key technologies of Industry 4.0 in AaHSP.
- Digital twins of AaHSP – modelling and simulation.
- Robotized assembly and handling systems. Collaborative robots in AaHSP.
- Structure of industrial robot (IR) degrees of freedom, a typical IR, components, workspace (handy and reach), drives, sensors
- Control of IR and security. Human-robot cooperation. Programming IR: on and off-line programming
- External sensors in robotized AaHS, tactile sensors and robot vision
- Grippers, manipulation grippers and technological tools, sensors of grippers.
- Standards and safety in robotized AaHS.
Tutorials – main topics:
• Basics of kinematic modeling: kinematics of IR, the connection between the speeds and accelerations of coordinates, generating a working moves, giving tasks, profiles, interpolations
• Controlling of small robots via PC
• Control of the rotary and linear servo axis for use in the automation.
• Manual workplace design using a computer – modelling and simulation.
• Modelling of production and assembly processes, design of experiments for optimization and simulation performing.
• Creating the product structure of the 3D product model by a computer on the base of model structure.
• Modelling of production and assembly processes in 3D.
Objectives and competences:
Goals:
- To teach the students the fundamentals of methodology used in the selection, design, analysis and evaluation of automated assembly and handling systems and processes (AaHSP), and about the integration thereof into the production process of the factories of the future
- Acquisition of basic knowledge for planning and integration of robotic AaHSP into the production process of the factories of the future.
Competences:
• The ability to select, design, analyse and evaluate automated and robotic AaHS, as well as the integration thereof into the production process.
• The ability to use modern approaches of analysis, modeling, optimization and simulation of production systems and processes.
• Understanding the economic aspects of automation and robotization of AaHS.
• Knowing the significance of standards and safety in robotic AaHS.
Intended learning outcomes:
Knowledge and understanding:
The student learns and understands:
• The fundamentals of automated assembly and handling systems and processes, rules and models.
• The fundamentals of robotics, the structures, relations, robotic applications in automated AaHSP.
• The fundumentals of AaHSP in the factories of the future.
• The fundamentals of gripper technology in automated AaHSP and the fundamentals of robotic grippers.
• The significance of standards and safety in robotic AaHSP.
Usage:
The students use the knowledge attained for planning and analysing of AaHSP, as well as for the integration thereof into the production process.
Reflection:
Using the presented methodologies and technologies in solving real AaHSP problems.
Transferrable skills – related to more than one course:
Using literature – hard copies and internets sources.
Problem identification and methods of problem solving.
The ability to plan and manage projects focused on designing assembly and handling systems.
Lecturer: Kos Leon, Vukašinović Nikola
Syllabus outline:
- Design levels, S-curve of product maturity, different models for product development
- Product (service) design requirements (according to the design levels and type of production)
- Product / system concepts variations. Patents search, intellectual property, TRIZ methodology – ideality, contradictions, system approach. Original design (the approach to problem identification and methods for problem definition)
- EU regulation and legislation as constraint and opportunity. CE mark, certificates for products, product safety, risk management, eco-design.
- Robust product/process design according to good practice in the automotive industry. Design methods: APQP, FMEA, SPC, MSA, CP, Poka Yoke.
- Module and system cost-efficient design (manufacturing, total, and lifecycle costs; fixed, and variable costs; material, personal, and capital costs).
- Selected product development methods: concurrent engineering (CE), set-based CE, design for six-sigma, design of experiments.
- Virtual and physical prototyping through product development. Product verification and validation.
- Design concepts invariant design, the influence of parameter and value interval consideration. Product development and support with PDM/PLM system, management of product variants, implementation of standardisation.
- Innovation design with the variation of working principles and derivations. Application of technical information system: document management, workflow, product modelling, knowledge management.
Objectives and competences:
- Goals: To present the design technique for different subject matters and products and for different phases of product development. The difference between product development in serial, small-batch, and one-of-a-kind production will be explained specifically, so the students can use their knowledge in the practice to determine the content and scope of work at the beginning of the design and development process according to the level of treatment: design or planning.
- Competences: The students learn the principles of defining the development and design process for a specific product. Based on the process requirements, they determine the execution of tasks, structured according to the »golden loop« model and some other models such as the automotive industry. All the starting points are employed that were taught in the Product design and development course and the Design methodology course. This knowledge qualifies the students to recognise the necessary methods and activities for an accelerated product development.
Intended learning outcomes:
- Knowledge and understanding: The students assimilate the fundamental knowledge about the methods in design techniques, enabling them to prepare the data and knowledge for different levels of design and variant planning.
- Usage: Direct use in planning and execution of details in the scope of design and planning.
- Reflection: In the phase of preparation for the execution of any project or design, it is important that all the data is prepared to reasonably ensure the quality of planning and design. A special emphasis is placed on the details related to the manufacturing technologies and natural processes/systems.
- Transferrable skills: The students learn the capability to recognise the different levels of planning and design through all product development phases. They have competences for proper selection and application of the method according to design level and phase in the product development process. The attained knowledge and assimilated methods make it possible to quickly master the development of objects and products.