Artificial Intelligence may not only be the most exciting field in computer science, but of science in general. In fact, the best scientists of the future might even be AIs themselves. Hardware soon will have more raw computational power (CP) than human brains, since CP per cent is still growing by a factor of 100-1000 per decade. And there is no reason to believe that general problem solving software similar to that of humans will be lacking: there already exist mathematically optimal (though not yet practical) universal problem solvers developed at IDSIA. And existing highly practical (but not quite as universal) AI already learn from experience, outperforming humans in more and more fields. For example, biologically plausible deep / recurrent artificial neural networks are learning to solve pattern recognition tasks that seemed infeasible only 10 years ago. Examples: images, handwriting, traffic signs, since 2011 even with superhuman performance - no end in sight. Even creativity has been formalized such that it can now be implemented on machines. The current developments in AI may soon lead to the end of history as we know it (more), and as an IS master student you can become part of this revolution.
Artificial Intelligence systems have knowledge, beliefs, preferences and goals, and they have informational as well as motivational attitudes. They observe, learn, communicate, plan, anticipate and commit. They are able to reason about other systems and their own internal states, to simulate and optimize their performance. AI systems react to dynamic situations adapting their capabilities through learning mechanisms, with a high degree of autonomy.
In this master program a wide variety of techniques will be taught, including intelligent robotics, artificial deep neural networks, machine learning, meta-heuristics optimization techniques, data mining, data analytics, simulation and distributed algorithms. The main courses are integrated with laboratory works where students have the possibility to use real robots and to practice with state of the art tools and methodologies. After the first few lectures of the basic Machine Learning course, AI master students will already know how to train self-learning artificial neural networks to recognize the images and handwritings to the right better than any other known method.
|Deep Learning Lab||3|
|Algorithms & Complexity||6|
|Computer Vision & Pattern Recognition*||6|
|Geometric Deep Learning*||3|
|Electives Fall semester||ECTS|
|Introduction to Partial Differential Equations||6|
|Simulation & Data Sciences Seminar||3|
|User Experience Design||6|
|Electives Spring semester||ECTS|
|Advanced Computer Architectures||6|
|Business Intelligence and Applications||6|
|Software Atelier: Simulation, Data Science & Supercomputing||6|
* This course will not be offered in the academic year 2017/18 for this Master programme.
They are all actively involved in EU, FNS, CTI and industry projects. Their research activities cover subjects such as deep learning in neural networks, intelligent and collective robotics, data mining, nature-inspired optimization, simulation, approximation algorithms and intelligent information retrieval. The coursework material, software tools and robots reflects the latest state of the art. Further opportunities will arise through the thesis which may open the path towards PhD studies.
Academic Director: Luca Maria Gambardella
Co-Director: Jürgen Schmidhuber
Admission to English-language graduate-level (Master) programmes at USI require a good command of the English idiom. Non-English native speakers applying for such programmes, or whose previous degree was obtained in another language, are required to provide an internationally acknowledged language certificate equal to the B2 level, as defined by the Common European Framework of Reference for language learning (CEFR), or equivalent (e.g. TOEFL, IELTS, etc.).
The B2 level on the CEFR corresponds to the following scores in internationally acknowledge exams:
|TOEFL||Computer based: 183
Internet based: 65
Paper based: 513
|Cambridge English||FCE (First Certificate English)|
|TOEIC||Listening & Reading: 785
Students admitted under the above mentioned condition (with the exception for the Master in Cognitive Psychology in Health Communication) must achieve a C1 competence in English within the maximum time required to obtain the Master's degree.
The level can be certified either by attending a language course offered at USI during the Fall and Spring semester, and by taking the final exam, or by providing an internationally acknowledged language certificate*.
* The C1 level on the CEFR corresponds to the following scores in internationally acknowledge exams:
|TOEFL||Internet based: 100|
|Cambridge English||CAE (Advanced certificate), grade C or above
BEC (Business English), grade C or above
|TOEIC||Listening & Reading: 945
As Lugano is located in the Italian-speaking part of Switzerland, students might be interested in acquiring the basics in Italian, in order to be able to get around in everyday life.
The Università della Svizzera italiana offers an Italian language study programme organized in various modules. All its non Italian-speaking students, researchers and professors are invited to participate free of charge.
Read all details about the Italian classes offered at USI, here.