Duration
30h Th
Number of credits
| Master in business engineering (120 ECTS) | 5 crédits | |||
| Extra courses intended for exchange students (Erasmus, ...) | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Objectives
The aim of this course is to introduce advanced, novel technologies and the state of the art in related research to masters students. These technologies and research are poised to play an increasingly important role in the digital age.
The main themes will revolve around:
- Blockchains
- Smartcontracts
- Cryptography
- AI (in particular, Reinforcement Learning)
- Ethics, Regulations and Legal Aspects
The topics to be covered are listed below. Note that they will be addressed from a mathematical (e.g. number theory, formal logic) and technical perspective, with links to management.
Participants will rely heavily on their expertise acquired from previous courses in
- Number theory and algebra
- Mathematical logic (first order and descriptive logics)
- Statistics (machine learning)
- Design and analysis of algorithms (and programming)
Topics
Blockchains & SmartContracts
- Revision of blockchains & smartcontracts(based INFO0934)
- Smartcontract technologies, including Ropsten, MetaMask
- Smartcontract programming in Solidity and Javascript
- Developing a smartcontract application in Ethereum
- Deploying smartcontracts on Ethereum and issuing transactions based on gas and ether
Cryptography and Hashing
- Relation to blockchains
- Foundations of cryptopgraphy (number theory)
- RSA algorithm
- SHA algorithm
- Elliptic Curve Digital Signature Algorithm (ECDSA)
- Cybersecurity (to be confirmed: guest speaker)
Semantic Web
- First Order Logic (FOL)
- Descriptive Logic (DL)
- Introduction to the Semantic Web
- Semantic Web standards: XML, RDF, RDFs
- Ontologies for knowledge representation and reasoning
- (if time permits) OWL standard and Protégé for ontology editing
- Reinforcement Learning, in particular Q-learning algorithm
- Deep Learning and Deep Reinforcement Learning
- AI & Game Theory Research (multi-agent interaction)
- Overview of recent breakthroughs (games of GO and minecraft)
- (if time permits) Generative Adversarial Networks (GAN)
Regulations, Ethics and Legal Aspects
- Given by guest speaker (to be confirmed)
- Regulating blockchains
- Bias in AI (machine learning)
- AI and anti-trust (competition law), algorithmic collusion
- Discussion of legal, judicial cases
Practical
There will be a course project, requiring participants to implement and deploy a smart contract on Ethereum using Solidity and Javascript. Necessary resources will be provided.
Participants will also be required to read, understand and present (discuss) recent scientific research articles.
The project and presentation will count towards the final grades.
Learning outcomes of the learning unit
- Understand the underlying principles and algebraic formulations of advanced methods, algorithms and technologies in digital business.
- Ability to apply these models in various managerial contexts.
- Synthesize various principles and algorithms introduced in the course and to develop a full-fledge applications/solutions (as part of the course project)
- Implement at least one of the technology (smart contract project)
- Formulate a strategy based on the skills to optimize the value of an organization
- Ability to perform research on and understand advanced topics in the field and to be informed on recent developments to adapt easily to changing requirements
- Communicate appropriately about text analytics projects/applications to various stakeholders
Prerequisite knowledge and skills
- Number theory and algebra
- Mathematical logic (first order and descriptive logics)
- Statistics (machine learning)
- Design and analysis of algorithms (and programming)
Planned learning activities and teaching methods
The course carries 5 credits and therefore requires 150 hours of work (1 credit = 30 hours).
Theory lectures = 18-22 hours
- Self-study for exam = approx. 70 hours
- Practical lectures = 9-12 hours
- Working on practical exercises and projects = approx. 80 hours
- Total = 150 hours (5 credits)
Mode of delivery (face-to-face ; distance-learning)
- Lectures (face-to-face)
- Practical (during lectures and as homework)
Recommended or required readings
- Scientific articles, to be communicated during the lectures.These will be provided on Lol@, the online learning platform.
Assessment methods and criteria
Continuous Evaluation with final exam:
- Practical project (40%)
- Paper presentation and discussion (10%)
- Final exam (50%)
Work placement(s)
Organizational remarks
Materials will be made available on Lol@, the online learning platform.
Contacts
Ashwin Ittoo, ashwin.ittoo@uliege.be
Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session
Teaching methods implemented : distance-learning
All lectures, starting from the 18th March till the end of the quadrimester, have been conducted digitally, i.e. via recorded videos.
Videos have been made available on Lol@ and communicated to students (http://lola.hec.uliege.be/course/view.php?id=554).
In addition, two interactive sessions have been also organized via life-size, one for the course contents and the other concerning the practical.
Furthermore, the practical on IoT (hardware-software interfacing) was dropped due to the confinement: 1) inability to order and purchase the required components; 2) inability to organize physical practical sessions.
Assessment subjects
All videos and notes (except Introduction), including MongoDB
A separate video to be made available on Lol@, highlighting important topics.
Assessment methods
a) Oral examination. Candidates will be examined on the theory theory (incl. mathematics related number theory) and practical (MongoDB queries). (40%)
b) Pyton/MongoDB Practical and Presentation. (60%)
Contacts
ashwin.ittoo@uliege.be