Detailed content

• The first year (M1) program
• M1S1 — First year first semes­ter at UNIGE
• M1S1 — First year first semes­ter at UTC
• M1S1 — First year first semes­ter at UPC
• M1S2 — First year sec­ond semes­ter at UPT
• The sec­ond year (M2) pro­gram (60 ECTS credits)
• M2S1 — Sec­ond year first semes­ter at UNIGE
• M2S1 — Sec­ond year first semes­ter at UTC
• M2S1 — Sec­ond year first semes­ter at UPC
• M2S2 — Mas­ter the­sis (22–26 weeks)

The list of cours­es is in line with the mobil­i­ty scheme sum­ma­rized below:

The first year (M1) program

#	Train­ing path	M1S1	M1S2	M2S1	M2S2
1	SoSE	UTC UPC	UPT	UNIGE	Final Mas­ter projects in col­lab­o­ra­tion with Indus­tries join­ing the EMSSE programme.
2	AOS	UNIGE UPC	UPT	UTC	Final Mas­ter projects in col­lab­o­ra­tion with Indus­tries join­ing the EMSSE programme.
3	AMS	UTC UNIGE	UPT	UPC	Final Mas­ter projects in col­lab­o­ra­tion with Indus­tries join­ing the EMSSE programme.

M1S1 — First year first semester at UNIGE

	ECTS	Teach­ing objectives
MACHINE LEARNING AND DATA ANALYSIS	9	Stu­dents will be pro­vid­ed with advanced skills relat­ed to machine learn­ing and data analy­sis. Stu­dents will learn insights on machine learn­ing and data analy­sis method­olo­gies with a series of real world applications.
COMPUTER SECURITY	9	Upon com­ple­tion of the course, stu­dents will be able to: explain the con­cepts of con­fi­den­tial­i­ty, avail­abil­i­ty, and integri­ty (CIA) as well as the con­cepts of threat, vul­ner­a­bil­i­ty, exploit and (cyber-)risk and (cyber-)risk mit­i­ga­tion; explain the strengths and weak­ness­es of cryp­to­graph­ic tech­niques as well as their role in pro­tect­ing data at rest and in tran­sit, in imple­ment­ing the con­cept of dig­i­tal sig­na­ture and in sup­port­ing the design of secu­ri­ty pro­to­cols; explain the secu­ri­ty mod­el of web browsers and iden­ti­fy the most rel­e­vant vul­ner­a­bil­i­ties of web appli­ca­tions; explain the caus­es and effects of buffer over­flows in exe­cutable pro­grams; explain the key prin­ci­ples of access con­trol in infor­ma­tion sys­tems and most rel­e­vant access con­trol mod­els and mechanisms.
SYSTEMS IDENTIFICATION	6	The goal of the course is to pro­vide method­olo­gies and tools for design­ing sys­tems mod­els to be used for con­trol, esti­ma­tion, diag­no­sis, pre­dic­tion, etc. Dif­fer­ent iden­ti­fi­ca­tion meth­ods are con­sid­ered, both in a “black box” con­text (where the struc­ture of the sys­tem is unknown), as well as in a “grey box” (uncer­tain­ty on para­me­ters) one. Meth­ods are pro­vid­ed for choos­ing the com­plex­i­ty of the mod­els, for deter­min­ing the val­ues of their para­me­ters, and to val­i­date them. More­over, state esti­ma­tion prob­lems are addressed and their con­nec­tions with con­trol and iden­ti­fi­ca­tion are con­sid­ered. Projects in lab relat­ed to the course top­ics are also developed.
SUSTAINABLE SYSTEMS MODELLING	6	The course pro­vides basics of sys­tems the­o­ry and con­trol, and the def­i­n­i­tion and fea­tures of the main mod­el­ling frame­works avail­able for com­plex sys­tems. Dis­crete-event mod­els, Markov chains, sin­gle queues and queue­ing net­works approach­es will be treat­ed both from a method­olog­i­cal point of view and with appli­ca­tions to sev­er­al con­texts. All the con­sid­ered mod­el­ling frame­works will specif­i­cal­ly be treat­ed with respect to the process sustainability.

M1S1 — First year first semester at UTC

30 ECTS among the fol­low­ing courses:

	ECTS	Teach­ing objectives
EXPERIMENTAL DATA ANALYSIS	6	This course is intend­ed to pro­vide the basics in prob­a­bil­i­ty, sta­tis­tics, data analy­sis and sig­nal pro­cess­ing, nec­es­sary for the dif­fer­ent dis­ci­plines taught in the Mas­ter’s pro­gram. The teach­ing will be based on the study of many con­crete cas­es. Detailed con­tent: — Basics of prob­a­bil­i­ty the­o­ry — Ran­dom sam­pling, esti­ma­tion — Con­fi­dence inter­vals and sig­nif­i­cance tests — Mul­ti­di­men­sion­al data analy­sis: prin­ci­pal com­po­nent analy­sis and auto­mat­ic clas­si­fi­ca­tion — Intro­duc­tion to sig­nal pro­cess­ing: Fouri­er analy­sis, con­vo­lu­tion, intro­duc­tion to filtering.
INTRODUCTION TO STOCHASTIC MODELLING	3	The objec­tive of this course is to give the prob­a­bilis­tic and sta­tis­ti­cal basics to Mas­ter 1 stu­dents to obtain a com­mon base of knowl­edge for com­put­er sci­ence, biol­o­gy and mechan­ics through the sim­u­la­tion of ran­dom sys­tems. This course cov­ers top­ics like Metrop­o­lis Algo­rithm, Sta­tis­tics, Reli­a­bil­i­ty, Sto­chas­tic Process­es, Crack Prop­a­ga­tion, Monte Car­lo Method, Main­te­nance, Markov Meth­ods, DNA Analysis
SCIENTIFIC COMPUTING	3	This course ppro­vides skills to rec­og­nize some typ­i­cal engi­neer­ing prob­lems, do the work of for­mat­ting equa­tions, and then solve them with Scilab.
CONTROL COMMANDS	3	This course first describes the main rep­re­sen­ta­tions of the behav­ior of lin­ear dynam­i­cal sys­tems. It then presents clas­si­cal con­trol struc­tures and the tech­niques for adjust­ing their con­trol. This course is lim­it­ed to the con­tin­u­ous-time approach.
SAFETY AND SECURITY IN SYSTEMS	3	The objec­tive of this course is to pro­vide the basis for meth­ods for eval­u­at­ing and pre­dict­ing the oper­a­tional safe­ty para­me­ters of sys­tems (reli­a­bil­i­ty, avail­abil­i­ty, main­tain­abil­i­ty, safe­ty) and the asso­ci­at­ed fun­da­men­tal con­cepts (fail­ure rate, MTTF, etc.).
STATISTICS FOR ENGINEERS	7	This course gives the­o­ret­i­cal and prac­ti­cal study of the basic con­cepts and meth­ods of sta­tis­tics with a view to its use in engi­neer­ing sci­ences. The goal is to intro­duce the basis of sta­tis­ti­cal infer­ence — Solve sim­ple and stan­dard sta­tis­ti­cal infer­ence prob­lems — Use clas­si­cal sta­tis­ti­cal tools such as lin­ear regres­sion and analy­sis of vari­ance — Imple­ment dif­fer­ent sta­tis­ti­cal meth­ods on real data, and inter­pret the results — Use sta­tis­ti­cal soft­ware R.
FRENCH – FOREIGN LANGUAGE	4	The pur­pose of this course, for a non-French-speak­ing stu­dent, is to acquire a min­i­mum lev­el of com­mu­ni­ca­tion in French. The abil­i­ty to com­mu­ni­cate will be empha­sized through activ­i­ties deal­ing with com­mon sit­u­a­tions of dai­ly life.
BIOMIMICRY OF SYSTEM OF SYSTEMS	3	This course aims to show the pow­er of bio­mimicry in solv­ing tech­no­log­i­cal prob­lems espe­cial­ly in the con­text of tech­no­log­i­cal sys­tems of sys­tems. The aim of this course is to train stu­dents in the bio­mimet­ic approach and to pro­vide them with the meth­ods / tools that will enable them to apply this approach on con­crete issues. Mul­ti­dis­ci­pli­nary groups of four stu­dents will work on inter­dis­ci­pli­nary top­ics to pro­pose a bio­mimet­ic solu­tion to a giv­en tech­no­log­i­cal prob­lem in rela­tion to the tech­no­log­i­cal sys­tems of sys­tems. Here are few exam­ples of top­ics: the devel­op­ment of new bio­mimet­ic algo­rithms, or exoskele­tons or bio­mimet­ic actu­a­tors. The stu­dents will be trained on the basis of exam­ples, show­ing them the involve­ment of bio­mimet­ic inno­va­tion in var­i­ous tech­no­log­i­cal sec­tors, and they will also be trained in the method­ol­o­gy of the bio­mimet­ic approach, the project approach, the main use­ful means of char­ac­ter­i­za­tion and the dif­fer­ent sources of bib­li­o­graph­ic mon­i­tor­ing of the sec­tor. Cours­es will show the sin­gu­lar­i­ty of the liv­ing com­pared to tech­nol­o­gy. The tuto­ri­als will allow to mon­i­tor the projects and to sup­port stu­dents in estab­lish­ing their final project, sci­en­tif­i­cal­ly substantiated.
APPLICATION OF MACHINE LEARNING IN MODELING IN ENGINEERING	3	Design engi­neer­ing today uses diverse sim­u­la­tion codes for analy­sis, explo­ration of design spaces, and opti­miza­tion. These dig­i­tal tools replace phys­i­cal test­ing. How­ev­er, they require long com­pu­ta­tion times, which are some­times incom­pat­i­ble with study dead­lines and con­straints. Arti­fi­cial intel­li­gence can be put to good use in this con­text by exploit­ing the com­pu­ta­tion­al results already obtained to pro­duce lighter and faster numer­i­cal mod­els, hope­ful­ly with a sim­i­lar lev­el of fideli­ty. This course explores machine learn­ing tech­niques for gen­er­at­ing meta-mod­els. The course is accom­pa­nied by prac­ti­cal work on the machine for the effec­tive imple­men­ta­tion of the algo­rithms stud­ied in this course.
SEMINARS AND COMMUNICATION	2	This course con­sists in assist­ing in all research sem­i­nars of the Heudi­asyc lab­o­ra­to­ry (approx­i­mate­ly 1 each two weeks). Next, the stu­dent has to review some papers of one sem­i­nar speak­er and do an oral pre­sen­ta­tion on the top­ic dur­ing which he has to sum­ma­rize the stud­ied prob­lem, the state of the art and the contribution.
SHORT INTERNSHIP – INITIATION TO RESEARCH ENVIRONMENT	5	Peri­od of obser­va­tion and dis­cov­ery of the pro­fes­sion­al envi­ron­ment of research and devel­op­ment in the pub­lic or pri­vate sec­tor, in line with the spe­cial­ty and the stu­den­t’s project. It takes place dur­ing the first year of the mas­ter’s degree (M1) over a peri­od min­i­mum of 4 weeks, at the end of the first semes­ter. Work car­ried out is the sub­ject of a writ­ten report and an oral defense.

M1S1 — First year first semester at UPC

	ECTS	Teach­ing objectives
DATA ACQUISITION & INSTRUMENTATION	6	The course con­sists of a set of lec­tures to intro­duce sen­sors and advanced elec­tron­ics sys­tems in the frame­work of mea­sure­ments, data acqui­si­tion sys­tems and instru­men­ta­tion tech­nolo­gies. As a con­se­quence, some lab ses­sions and per­son­al work super­vised by fac­ul­ty to devel­op a project. The course will be PBL ori­ent­ed. In par­tic­u­lar, the course will be PBL ori­ent­ed in order to design, sim­u­late and imple­ment a data acqui­si­tion sys­tems (DAS) devot­ed to sense a set of envi­ron­ment or mete­o­ro­log­i­cal variables.
DATA ANALYSIS & PATTERN RECOGNITION	6	This course offers an intro­duc­tion to arti­fi­cial intel­li­gence meth­ods includ­ing unsu­per­vised tech­niques (explorato­ry analy­sis, dimen­sion­al­i­ty reduc­tion, clus­ter­ing algo­rithms) and super­vised meth­ods (clas­si­fi­ca­tion, regres­sion). Spe­cif­ic mod­ules of the course are devot­ed to the sta­tis­ti­cal eval­u­a­tion of the per­for­mance of an AI mod­el. Rel­e­vant con­cepts as mod­el over­fit­ting and bal­anced train­ing data are explained along with mod­el selec­tion approach­es to find a bal­ance between model’s com­plex­i­ty and performance.
SYSTEMS MODELING	6	The course aims to under­stand mod­els of phys­i­cal sys­tems based on par­tial dif­fer­en­tial equa­tions, con­tin­u­um mechan­ics and con­sti­tu­tive mod­els. It intro­duces the weak for­mu­la­tion of phys­i­cal laws and the con­ti­nu­ity con­di­tions they imply when deal­ing with mul­ti­physics prob­lems, as well as reg­u­lar­i­ty of the solu­tions and how to estab­lish the com­plex­i­ty of the prob­lem (e.g. the com­pu­ta­tion­al cost of numer­i­cal sim­u­la­tions). Iden­ti­fy mul­ti-scale fea­tures of phys­i­cal prob­lems, select appro­pri­ate scale sep­a­ra­tion oper­a­tors and small-scale models.
COMPUTER VISION	6	The course pro­vides an intro­duc­tion to dig­i­tal image pro­cess­ing algo­rithms (image han­dling, fil­ter­ing, seg­men­ta­tion, etc.) as well as the use of com­put­er vision meth­ods such as con­vo­lu­tion­al neur­al net­works for image clas­si­fi­ca­tion (deep learn­ing). A final block is devot­ed to offer an Overview of vir­tu­al real­i­ty (VR) hard­ware and soft­ware to learn dif­fer­ent ways to get start­ed with this technology.
TECHNOLOGY INNOVATION 1	6	This course aims to pro­vide stu­dents with an expe­ri­ence-based intro­duc­tion into the tech­nol­o­gy-based inno­va­tion. A real-life sim­u­la­tion of the process that inno­va­tors go through when con­sid­er­ing a tech­no­log­i­cal busi­ness oppor­tu­ni­ty will be per­formed. To do so, the dif­fer­ent steps of the inno­va­tion process will be con­sid­ered. In par­tic­u­lar, the phas­es con­sid­ered will be: (1) analy­sis of a tech­nol­o­gy oppor­tu­ni­ty, (2) def­i­n­i­tion of a pro­pos­al, and (3) pre­sen­ta­tion of a pro­pos­al. At the end of the course, the stu­dent will be able to use the tools analy­sis of analy­sis that are used in the inno­va­tion world to assess a tech­no­log­i­cal busi­ness oppor­tu­ni­ty and to present the results appropriately.

M1S2 — First year second semester at UPT

30 ECTS among the fol­low­ing courses:

	ECTS	Teach­ing objectives
OPERATIONS RESEARCH	6	The objec­tives of this course are to intro­duce among future engi­neers of the fun­da­men­tal notion of algo­rith­mic com­plex­i­ty and to teach stu­dents a num­ber of graph-based tools to address com­bi­na­to­r­i­al prob­lems. The fol­low­ing top­ics are cov­ered: com­bi­na­to­r­i­al opti­miza­tion, algo­rithms, graphs, Data Struc­ture, com­plex­i­ty, intro­duc­tion to lin­ear pro­gram­ming, etc.
ADVANCED MACHINE LEARNING	3	This course will start with a short prob­a­bil­i­ty review includ­ing: - Probability/Random variables/Random vec­tors, Con­di­tion­al distribution/expectation, Gauss­ian ran­dom vec­tors, Types of convergence/limit the­o­rems, Simulation/ Monte Car­lo Next, will focus on Sam­pling: Sam­pling from gen­er­al dis­tri­b­u­tions, Sam­pling from nor­mal dis­tri­b­u­tion, Order sta­tis­tics The third part will include Sta­tis­ti­cal esti­ma­tion: Esti­ma­tors and their prop­er­ties, Meth­ods of esti­ma­tion, Eval­u­a­tion of esti­ma­tors, Inter­val esti­ma­tion, Boot­strap in sta­tis­ti­cal prob­lems In the fol­low­ing, there is Test­ing sta­tis­ti­cal hypoth­e­sis chap­ter: For­mu­la­tion of the hypoth­e­sis test­ing, Like­li­hood ratio test, P‑value, Chi-square tests Last part includes Regres­sion lin­ear and mul­ti­lin­ear cov­er­ing: Lin­ear regres­sion, Mul­ti­lin­ear regres­sion and an appli­ca­tion, Ker­nel regres­sion, Shrink­age, Ridge regres­sion, Las­so VI. Prin­ci­pal Com­po­nent Analy­sis, PCA Algo­rithm, PCA Eval­u­a­tion, An application
INDUSTRIAL AUTOMATION	6	This course intro­duces essen­tial mod­el­ing and method­olog­i­cal tools for address­ing deci­sion-mak­ing and man­age­ment chal­lenges with­in indus­tri­al sys­tems, includ­ing plan­ning, sched­ul­ing, and con­trol prob­lems using the ANSI/ISA-95 stan­dard. It empha­sizes the Man­u­fac­tur­ing Exe­cu­tion Sys­tem (MES) and cov­ers Pro­gram­ma­ble Log­ic Con­troller (PLC) with lad­der pro­gram­ming. By the end, stu­dents can adept­ly posi­tion indus­tri­al automa­tion issues with­in ANSI/ISA-95, for­mal­ize, and solve prob­lems using appro­pri­ate meth­ods and tools, gain­ing prac­ti­cal skills in PLC lad­der pro­gram­ming and sched­ul­ing tech­niques for indus­tri­al automation.
SUSTAINABILITY AND CIRCULAR ECONOMY	3	The objec­tive of this course is to describe and under­stand the cir­cu­lar econ­o­my approach; raise the chal­lenges, bar­ri­ers and oppor­tu­ni­ties offered by this new par­a­digm and eval­u­ate the systems/processes/products that imple­ment cir­cu­lar­i­ty from a life cycle per­spec­tive and con­sid­er­ing the three pil­lars of sus­tain­abil­i­ty, envi­ron­men­tal (LCA), eco­nom­ic (LCC) and social (SLCA)
SOFTWARE ENGINEERING FOR SYSTEMS MODELLING	3	This soft­ware engi­neer­ing course focus­es on sys­tems mod­el­ing method­olo­gies for suc­cess­ful engi­neer­ing projects. It starts with an intro­duc­tion to the Uni­fied Mod­el­ing Lan­guage (UML), pro­vid­ing a sol­id foun­da­tion in its prin­ci­ples and appli­ca­tions. The course then tran­si­tions to the Sys­tems Mod­el­ing Lan­guage (SysML), designed specif­i­cal­ly for sys­tems engi­neer­ing. Par­tic­i­pants will gain prac­ti­cal skills through hands-on exer­cis­es and real-world exam­ples, par­tic­u­lar­ly in the con­text of the pro­duc­tion industry.
SUSTAINABLE SYSTEMS ENGINEERING	3	The Sus­tain­able Sys­tems Engi­neer­ing course explores advanced meth­ods for man­ag­ing com­plex sys­tems with a focus on sus­tain­abil­i­ty. Par­tic­i­pants will study mod­el­ing, plan­ning, and con­trol tech­niques for trans­porta­tion sys­tems, logis­tic net­works, and ener­gy sys­tems. Empha­sis is placed on defin­ing sus­tain­abil­i­ty-relat­ed per­for­mance indi­ca­tors with­in man­age­ment meth­ods, con­sid­er­ing both envi­ron­men­tal and eco­nom­ic impacts. The course eval­u­ates the eco­nom­ic sus­tain­abil­i­ty of the meth­ods them­selves, address­ing chal­lenges at strate­gic, tac­ti­cal, and oper­a­tional lev­els. Par­tic­i­pants will gain insights into holis­tic and eco­nom­i­cal­ly viable approach­es to sus­tain­able sys­tems engineering.
INTRODUCTION TO ALBANIAN LANGUAGE AND CULTURE	3	This course (2hours/week) is designed to famil­iar­ize EMSSE stu­dents desir­ing to learn more on the rich lin­guis­tic and cul­tur­al her­itage of Alba­nia. Through inter­ac­tive lessons, stu­dents explore the fun­da­men­tals of the Alban­ian lan­guage, includ­ing its unique gram­mar, vocab­u­lary, and pro­nun­ci­a­tion. Addi­tion­al­ly, they delve into the diverse tra­di­tions, cus­toms, and his­to­ry that shape Alban­ian cul­ture, gain­ing insights into its lit­er­a­ture, arts, cui­sine, and soci­etal norms.
COMPUTER VISION	3	The course offers an intro­duc­tion to dig­i­tal image pro­cess­ing and com­put­er vision algo­rithms. A first part is devot­ed to image pre-pro­cess­ing algo­rithms includ­ing image han­dling, math­e­mat­i­cal oper­a­tions and noise fil­ter­ing. A sec­ond block will be devot­ed to object seg­men­ta­tion and extrac­tion of region­al fea­tures. The final part of the course deals with machine learn­ing algo­rithms applied to the analy­sis and clas­si­fi­ca­tion of images.
SIMULATION FOR SUSTAINABLE SYSTEMS	3	The course offers an intro­duc­tion to dis­crete-event sim­u­la­tion sys­tems. A first block is devot­ed to mod­el­ling, sim­u­la­tion and analy­sis using Petri Net mod­els to offer a qual­i­ta­tive descrip­tion of the dynam­ics of a dis­crete, dis­trib­uted sys­tem. The sec­ond part will focus in quan­ti­ta­tive, sta­tis­ti­cal approach­es using dis­crete-event sim­u­la­tion sys­tems (DES) to mod­el queue dynam­ics, sys­tem oper­a­tion and resource utilization.
LABORATORY OF SYSTEMS MODELING LANGUAGE	3	This lab­o­ra­to­ry focus­es on hands-on appli­ca­tion of Sys­tems Mod­el­ling Lan­guage (SysML) tech­niques and tools. Par­tic­i­pants will active­ly engage in prac­ti­cal exer­cis­es aimed at mas­ter­ing SysML for effec­tive sys­tem mod­el­ing. The course empha­sizes the appli­ca­tion of SysML in real-world sce­nar­ios, allow­ing par­tic­i­pants to gain valu­able expe­ri­ence in uti­liz­ing SysML tools to mod­el com­plex sys­tems. Through inter­ac­tive lab ses­sions, par­tic­i­pants will devel­op pro­fi­cien­cy in trans­lat­ing con­cep­tu­al ideas into tan­gi­ble SysML mod­els, fos­ter­ing a prac­ti­cal under­stand­ing of sys­tems mod­el­ing lan­guage in diverse contexts.
LABORATORY OF SUSTAINABLE SYSTEMS ENGINEERING	3	Par­tic­i­pants will lever­age method­olo­gies from the method­olog­i­cal cours­es to design and imple­ment solu­tions for deci­sion-mak­ing in com­plex sys­tems like logis­tics, trans­port, and renew­able ener­gy pro­duc­tion. The empha­sis is on apply­ing opti­miza­tion tech­niques based on math­e­mat­i­cal for­mu­la­tions to make deci­sions at strate­gic, tac­ti­cal, and oper­a­tional/re­al-time lev­els. Through hands-on activ­i­ties, par­tic­i­pants will address sus­tain­abil­i­ty chal­lenges, explor­ing applica­tive exam­ples and real case stud­ies. The course aims to cul­ti­vate deci­sion-mak­ing skills, pro­vid­ing a com­pre­hen­sive under­stand­ing of sus­tain­able sys­tems engineering.

The second year (M2) program (60 ECTS credits)

The sec­ond year, M2, will take place in the three EU HEIs with respect to the choice of the aca­d­e­m­ic path. Here­inafter, a list of cours­es offered for the dif­fer­ent paths at the three EU HEIs is presented.

M2S1 — Second year first semester at UNIGE

30 ECTS among the fol­low­ing courses:

	ECTS	Teach­ing objectives
SUSTAINABLE LOGISTIC SYSTEMS PLANNING	6	The course aims to pro­vide method­olo­gies and tools for opti­miz­ing and con­trol­ling logis­tic sys­tems (inter­modal net­works, con­tain­er ter­mi­nals, logis­tic cen­ters) with spe­cif­ic focus on the sus­tain­abil­i­ty of the con­sid­ered process­es. Refer­ring to the plan­ning and orga­ni­za­tion of logis­tic sys­tems, the stu­dent will learn how to: — iden­ti­fy the deci­sion prob­lem type — define the most appro­pri­ate math­e­mat­i­cal mod­el — define the most ade­quate solu­tion method­ol­o­gy — choose a soft­ware solu­tion for the prob­lem — dis­cuss the prob­lem relevance/effects.
SYSTEM OF SYSTEMS OPTIMISATION AND CONTROL	6	The course aims at pro­vid­ing mod­el­ing and method­olog­i­cal approach­es to sens­ing, actu­a­tion, and con­trol in order to describe and ana­lyze a Sys­tem of Sys­tems. Approach­es to take deci­sions based on the avail­able data in a dis­trib­uted, pre­dic­tive and/or adap­tive man­ner are includ­ed in the course. The stu­dent will approach Sys­tem of Sys­tems in dif­fer­ent applica­tive con­texts, such as smart pow­er grids, con­nect­ed autonomous vehi­cles and pla­toon­ing, ener­gy effi­cient build­ings, dis­trib­uted logis­tics, and envi­ron­men­tal monitoring.
TRUSTWORTHY ARTIFICIAL INTELLIGENCE	6	The aim of this course is to pro­vide grad­u­ate stu­dents with fun­da­men­tal and advanced con­cepts on the secu­ri­ty of machine learn­ing and trust­wor­thy arti­fi­cial intel­li­gence. Part 1 of the course intro­duces the fun­da­men­tals of the secu­ri­ty of machine learn­ing, the relat­ed field of adver­sar­i­al machine learn­ing, and some prac­ti­cal tech­niques to assess the vul­ner­a­bil­i­ty of machine-learn­ing algo­rithms and to pro­tect them from adver­sar­i­al attacks. Part 2 intro­duces the inter­na­tion­al reg­u­la­tions behind the so called “trust­wor­thy AI”, and the main tech­niques to design robust machine-learn­ing algo­rithms which are fair, pri­va­cy pre­serv­ing and whose oper­a­tion can be explained at some extent to the final users. The course uses appli­ca­tion exam­ples includ­ing object recog­ni­tion in images, bio­met­ric recog­ni­tion, spam fil­ter­ing, and mal­ware detection
PRODUCTION SYSTEMS	6	Under the title ‘Pro­duc­tion Sys­tems’ one can place very many dif­fer­ent prob­lems. This course is relat­ed with the decom­po­si­tion of a plan­ning and con­trol prob­lem of a pro­duc­tion sys­tem in dif­fer­ent sub­prob­lems. For any of the sub­prob­lems after an analy­sis process, a set of solv­ing tech­niques will be con­sid­ered. Such solv­ing tech­niques have to be inte­grat­ed in the solu­tion of the ‘main’ pro­duc­tion problem.
WORKSHOP PROJECT	6	Project work with companies
METHODS AND MODELS FOR DECISION SUPPORT	6	The course aims at intro­duc­ing the mod­eliza­tion and solu­tion tools for com­plex deci­sion prob­lems: meth­ods based on inte­ger pro­gram­ming mod­els, heuris­tics and meta­heuris­tics for com­bi­na­to­r­i­al opti­miza­tion prob­lems, the PERT method for Project Man­age­ment are stud­ied. Final­ly fun­da­men­tal con­cepts for solv­ing mul­ti-cri­te­ria deci­sion prob­lems are intro­duced. Appli­ca­tions to man­u­fac­tur­ing plan­ning and sched­ul­ing and logis­tics (net­work flow, loca­tion and vehi­cle rout­ing) will be considered.
TECHNOLOGIES FOR WIRELESS NETWORKS	6	The course aims to pro­vide a frame­work for all major net­work tech­nolo­gies that use wire­less (wire­less) trans­mis­sions, con­sid­er­ing appli­ca­tion areas and archi­tec­tures both from a struc­tur­al and pro­to­col­lary point of view. The result of learn­ing is to give the stu­dent, ori­ent­ed to a spe­cif­ic field of Engi­neer­ing, the abil­i­ty to under­stand the dif­fer­ent tech­nolo­gies of wire­less net­works and make effec­tive design choic­es for their effec­tive use.

M2S1 — Second year first semester at UTC

30 ECTS among the fol­low­ing courses:

	ECTS	Teach­ing objectives
ADVANCED SYSTEM ENGINEERING	3	The aim of this mod­ule is to intro­duce the main design tech­niques for depend­able sys­tems, par­tic­u­lar­ly for the safe­ty-crit­i­cal sys­tems as sub­stan­tial part of sys­tem engi­neer­ing. Pro­gram The dif­fer­ent method­olog­i­cal aspects con­cern­ing the design of depend­able sys­tems will be intro­duced: - Hard­ware redun­dan­cy: 1ooN archi­tec­tures, vot­ers PooN - Infor­ma­tion­al redun­dan­cy: errors detec­tion and cor­rec­tion, cod­ed proces­sors, appli­ca­tion to dis­trib­uted sys­tems - Effects of uncer­tain­ty - Robust design, reli­a­bil­i­ty of struc­tures - Fault tol­er­ance, fault removal - Fail-safe and fail-oper­a­tional systems
MODELING AND PROPAGATION OF UNCERTAINTIES	3	Uncer­tain­ties are present at all lev­els in the analy­sis and mod­el­ing of com­plex sys­tems. In par­tic­u­lar, one can dis­tin­guish between aleato­ry uncer­tain­ties, induced by the vari­abil­i­ty of stud­ied phe­nom­e­na, and epis­temic uncer­tain­ties due to imper­fect­ness of knowl­edge. The two clas­si­cal for­malisms for mod­el­ing uncer­tain­ties and prop­a­gat­ing them in rea­son­ing and com­pu­ta­tion mech­a­nisms are Prob­a­bil­i­ty The­o­ry and the set-mem­ber­ship approach (includ­ing Inter­val Analy­sis). More recent­ly, the the­o­ry of belief func­tions, which extends these two approach­es, has been devel­oped. This course intro­duces the the­o­ret­i­cal foun­da­tions of these three for­malisms, as well as the main prac­ti­cal meth­ods allow­ing for their appli­ca­tion in com­plex sys­tem engineering.
OPTIMISATION	3	This course intro­duces dif­fer­ent meth­ods and tools used for opti­miza­tion prob­lems. This course has two main com­po­nents: - Lin­ear opti­miza­tion through lin­ear pro­gram­ming, dual­i­ty, inte­ger lin­ear pro­gram­ming, branch and bound meth­ods, heuris­tic approach­es. - Non lin­ear opti­miza­tion in con­tin­u­ous vari­ables: uncon­strained prob­lems solver, first and sec­ond order prob­lems under con­straints, opti­mal­i­ty con­di­tions, solu­tion meth­ods with/without Lagrangian approach
WORKSHOP PROJECT	6	In this course, mul­ti-skills groups com­posed of 4 to 6 stu­dents will work on a project linked to the three Mas­ter spe­cial­iza­tions. The objec­tive is to apply the pre­vi­ous­ly stud­ied con­cepts, meth­ods and tools on a project while encour­ag­ing inter­ac­tions with the oth­er stu­dents in the group which have skills in oth­er domains. In this work­shop, the stu­dents will also be trained to mul­ti­dis­ci­pli­nary engi­neer­ing by con­sid­er­ing the spe­cif­ic con­straints to each field. Each project will be super­vised by a project leader (aca­d­e­m­ic or indus­tri­al) and a teach­ing staff com­posed of researchers from dif­fer­ent fields with skills in rela­tion with the project. The work­shop inte­grates sev­er­al aspects as sys­tems design, modeling/simulation or devel­op­ment and char­ac­ter­i­za­tion of exper­i­men­tal devices.
ADVANCES IN STATISTICAL MACHINE LEARNING	3	Large datasets are avail­able today on the Web, for instance from user-gen­er­at­ed con­tent (col­lab­o­ra­tive con­tent cre­ation as on Wikipedia, shar­ing infor­ma­tion as on Flickr, Face­book or Twit­ter) or nav­i­ga­tion logs col­lect­ed by Web­sites. The domain of sta­tis­ti­cal machine learn­ing pro­vides tools to exploit large datasets to build explana­to­ry or pre­dic­tive mod­els. The recent advances in this field, which can deal with large-scale, het­ero­ge­neous and com­plex data are nowa­days impor­tant tools in many appli­ca­tion domains such as image pro­cess­ing, infor­ma­tion retrieval or nat­ur­al lan­guage pro­cess­ing. In this lec­ture, we will present the fun­da­men­tal tech­niques of sta­tis­ti­cal machine learn­ing, the recent approach­es to deal with large amounts of com­plex data, as well as some prac­ti­cal applications.
DEEP LEARNING	3	This course presents an overview of deep learn­ing tech­niques, from the point of view of sta­tis­ti­cal learn­ing, and aims to enable their imple­men­ta­tion for solv­ing prac­ti­cal prob­lems. The con­cepts dis­cussed in class will be put into prac­tice dur­ing tuto­ri­als and lab ses­sions. Pro­gram - Con­cepts of sta­tis­ti­cal learn­ing, - Basic ingre­di­ents of deep learn­ing: mul­ti-lay­er per­cep­tron, con­vo­lu­tion­al net­works, back­prop­a­ga­tion, fit­ting cri­te­ria, reg­u­lar­iza­tion, - Imple­men­ta­tion: sto­chas­tic gra­di­ent, nor­mal­iza­tion, ini­tial­iza­tion, - Rep­re­sen­ta­tion­al learn­ing, auto-encoders, gen­er­a­tive mod­els, - Recur­rent models
MODELLING AND OPTIMIZING DISCRETE SYSTEMS	3	Sev­er­al opti­mi­sa­tion prob­lems in trans­porta­tion and logis­tics sys­tems are dis­crete. One can cite the vehi­cle rout­ing prob­lem, plan­ning prob­lems and local­iza­tion prob­lems. They belong to com­bi­na­to­r­i­al opti­miza­tion, which is an active area of applied math­e­mat­ics. This course presents the method­olo­gies for solv­ing them which com­bine log­ic, lin­ear pro­gram­ming and algo­rith­mic meth­ods. After this course the stu­dent will be aware of the fron­tier between the prob­lems that can be solved exact­ly and the ones that can only be solved approximately.
INTRODUCTION TO DECISION IN UNCERTAIN AND MULTI-CRITERIA ENVIRONMENTS	3	This course intro­duces dif­fer­ent views on how to solve deci­sion prob­lems in uncer­tain and mul­ti-cri­te­ria envi­ron­ment. The course will be split in two dis­tinct parts. The first part will take an axiomat­ic point of view, as often adopt­ed in arti­fi­cial intel­li­gence and oper­a­tions research, while the sec­ond part will con­sid­er the deci­sion prob­lem from a math­e­mat­i­cal pro­gram­ming and opti­miza­tion view­point. The course will focus on one-shot deci­sion, leav­ing the case of sequen­tial deci­sions for fur­ther inves­ti­ga­tion. Exam­ples and exer­cis­es will be giv­en dur­ing the course, to illus­trate the dif­fer­ent concepts
INTRODUCTION TO OPTIMIZATION UNDER UNCERTAINTY	3	Most real-world opti­miza­tion prob­lems involve uncer­tain data at some lev­el. Neglect­ing the pres­ence of uncer­tain­ty in opti­miza­tion prob­lems can lead to erro­neous­ly iden­ti­fy infea­si­ble or bad-qual­i­ty solu­tions as fea­si­ble or opti­mal, thus com­pro­mis­ing the deci­sion process. This course intro­duces Robust Opti­miza­tion (RO). Robust Opti­miza­tion (RO) is a method­ol­o­gy for deal­ing with the pres­ence of uncer­tain data in opti­miza­tion prob­lems that has known a wide suc­cess in the last years, espe­cial­ly thanks to its com­pu­ta­tion­al tractabil­i­ty. We will go through the fun­da­men­tals of RO, in par­tic­u­lar focus­ing on so-called car­di­nal­i­ty con­strained uncer­tain­ty sets and their appli­ca­tion in Mixed Inte­ger Lin­ear Pro­gram­ming. Dur­ing the course, exam­ples and exer­cis­es about real-world opti­miza­tion prob­lems sub­ject to uncer­tain­ty will be provided.
GRAPH LEARNING	3	This course aims to raise aware­ness among mas­ter’s stu­dents of the con­nec­tion between graph the­o­ry and machine learn­ing, essen­tial­ly via the notion of infer­ence graphs, and to teach them a cer­tain num­ber of math­e­mat­i­cal tools based on opti­miza­tion and sta­tis­tics enabling graph min­ing. UV train­ing objec­tive: At the end of the course, the stu­dent will be able to apply math­e­mat­i­cal tools by devel­op­ing com­plex mod­els and meth­ods for deci­sion sup­port for prob­lems involv­ing large mass­es con­stant­ly evolv­ing data.
ROBOT VISION	3	This course presents basics con­cepts on robot vision. It cov­ers a wide range of image pro­cess­ing meth­ods going from low lev­el (pix­els lev­el), through mid-lev­el (visu­al prim­i­tives lev­el) up to high lev­el (objects lev­el) algo­rithms applied to mobile robot­ic appli­ca­tions (Unmaned Area Vehicules (UAVs) and Wheeled robots). The con­cepts taught in course are put into prac­ti­cal ses­sions though robot­ic plat­forms with embed­ded sensors.

M2S1 — Second year first semester at UPC

30 ECTS among the fol­low­ing courses:

	ECTS	Teach­ing objectives
ADVANCED MANUFACTURING	6	This course intro­duces stu­dents to addi­tive and sub­trac­tive man­u­fac­tur­ing tech­nolo­gies and to imple­ment prod­uct char­ac­ter­i­za­tion pro­ce­dures. It empha­sizes the appli­ca­tion of reverse engi­neer­ing tech­niques for design­ing and man­u­fac­tur­ing func­tion­al parts and pro­to­types, along with the abil­i­ty to design man­u­fac­tur­ing process­es using inno­v­a­tive non-con­ven­tion­al meth­ods. Stu­dents learn to employ tools for opti­miz­ing val­ues of para­me­ters influ­enc­ing man­u­fac­tur­ing process­es and gain pro­fi­cien­cy in ana­lyz­ing process qual­i­ty based on the func­tion­al prop­er­ties of the man­u­fac­tured parts.
MECHATRONICS	6	This course allows stu­dents to inte­grate elec­tric­i­ty, elec­tron­ics, com­put­er sci­ence, and com­mu­ni­ca­tions tech­nolo­gies into the design of mechan­i­cal sys­tems. It focus­es on automat­ing the oper­a­tion of mechan­i­cal sys­tems and estab­lish­ing effec­tive com­mu­ni­ca­tion inter­faces with their sur­round­ing envi­ron­ment. The empha­sis is on devel­op­ing the capa­bil­i­ty to design mecha­tron­ic sys­tems tai­lored to the spe­cif­ic require­ments of a giv­en prod­uct, pro­vid­ing stu­dents with a com­pre­hen­sive skill set at the inter­sec­tion of mechan­i­cal engi­neer­ing and advanced dig­i­tal and con­trol technologies.
IOT SENSORS & MEMS	6	The aim of this course is to train stu­dents in meth­ods to design and use intel­li­gent sen­sor sys­tems and their con­nec­tion to the Inter­net-of-Things, with spe­cial empha­sis to Micro-Electro­mechan­i­cal Sys­tems (MEMS). The course cov­ers the fun­da­men­tals of intel­li­gent sen­sor sys­tems, includ­ing sig­nal acqui­si­tion stages, micro­elec­tron­ics, ampli­fiers, MEMS (Micro-Elec­tro-Mechan­i­cal Sys­tems), micro­fab­ri­ca­tion process­es, and the imple­men­ta­tion of dig­i­tal sig­nal pro­cess­ing on microcontrollers.
PLANT MONITORING & FAULT DETECTION	6	This course pro­vides an intro­duc­tion to the field of fault detec­tion and diag­no­sis in process­es, indus­tri­al sys­tems, and struc­tures, with a focus on uti­liz­ing data-based tech­niques such as sta­tis­ti­cal meth­ods and ana­lyt­i­cal redun­dan­cy mod­els. Stu­dents will gain the skills to design and imple­ment fault detec­tion and diag­no­sis sys­tems for var­i­ous appli­ca­tions. Spe­cif­ic com­pe­tences include under­stand­ing data-based and mod­el-based meth­ods, and pro­fi­cien­cy in using data-based meth­ods for detect­ing and diag­nos­ing defects or dam­ages in struc­tures. Upon com­ple­tion, stu­dents will be equipped to apply these tech­niques in prac­ti­cal sce­nar­ios involv­ing fault super­vi­sion and management.
ROBOTIC SYSTEMS	6	This course pro­vides a com­pre­hen­sive under­stand­ing of con­tem­po­rary robot­ics devel­op­ment, cov­er­ing the oper­a­tion, pro­gram­ming, and appli­ca­tions of indus­tri­al manip­u­la­tor robots. Stu­dents will gain the abil­i­ty to pro­fi­cient­ly pro­gram indus­tri­al robot manip­u­la­tors, as well as acquire insights into the oper­a­tion and appli­ca­tions of mobile robots with­in indus­tri­al envi­ron­ments. Addi­tion­al­ly, the course focus­es on devel­op­ing the skills need­ed to sim­u­late and opti­mize pro­duc­tive process­es that incor­po­rate robot­ic ele­ments, enhanc­ing stu­dents’ capa­bil­i­ties in inte­grat­ing robot­ics effec­tive­ly into indus­tri­al scenarios.Top of Form

M2S2 — Master thesis (22–26 weeks)

One EU HEI will be respon­si­ble of the intern­ship. An addi­tion­al tutor from anoth­er EMSSE HEI will also sup­port this work.