Appeal to Students
   The most important goal of this program is to make sure our students find interesting and well-paying jobs in different areas of artificial intelligence, machine learning and big data analysis. We plan to have a 100% placement rate upon graduation. This would be the most objective and fair evaluation of our program and of our efforts.  
Our students will simultaneously improve/learn/study in three important areas/disciplines. First, big data and artificial intelligence. Second, finance, economics and financial economics. Third, English speaking, writing and presentation skills. All of these would be applied to current events.
   Every course will have one coordinator/professor and at least three guest lecturers — professionals of highest calibre working in finance, economics or law, in different cities, from New-York, London, Frankfurt to Moscow and Dubai. They will travel to Yerevan for a few days to give several lectures. All their profiles will be available on our website. 
   It is important to remember that machine learning has gone through three stages of progress. In the first stage/era, machines could just count things (e.g. calculators). In the second stage/era, machines were programmable, and almost everything is programmable today — from TVs and dishwashers to cars and airplanes. 
   We are now living at the end of the second stage/era of machine learning and are entering the third era — of Artificial Intelligence  — when the machine is not simply programmable, but it learns from the past, adapts to the present and tries to predict the future, just like humans do. The crucial part of this is that machines, unlike people, are unbiased. 
   Human brains are heuristic — prefer shortcuts— and incapable of quickly, correctly and optimally processing all the data/information currently available. History teaches us that humans tend to make very stupid decisions, because of systemic errors, prejudices and cognitive biases. There is a lot of research proving that normally bright people can make very irrational decisions due to “tunnel vision, tribalism and other mental biases” (we will learn and study these biases in our Behavioural Finance and Economics course). 
   Artificial intelligence can offset these biases by adding objectivity to human decision-making.  Machines draw together all available information and form millions of hypotheses, and then test them with all the data. Machines with artificial intelligence learn over time, figure out what data is reliable (e.g. via back-testing), and that’s part of the learning process. These machines can detect trends and determine causalities faster and better than humans.
   Finance is probably the only field which has such a rich, diverse, reliable, comparable and readily available collection of data. We don't have to waste our time on collecting and cleaning databases. Instead, we will focus on data analysis, modelling and machine learning. Once students build and improve their knowhow of artificial intelligence and big data analysis in finance, they can apply it to any other field — health care, logistics, operations management and so on.
   This program is modern and carefully structured. Students will need to work very hard and be disciplined. We will be investing a lot of our time, efforts and money into this program because we want our students to be among the best in the world. We will, therefore, be very demanding. 
News
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RAU Opens a New Teaching Lab
2612/19
The laboratory now has the necessary equipment to conduct practical courses for students of the Department of Telecommunications.
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Daniel Frynta and Pavel Němec Lecturing on Zoology
1705/19
The delegation from the Department of Zoology, Faculty of Science at Charles University in Prague, Czech Republic, visited RAU within the Erasmus+ program.
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RAU Students and Lecturers at PROMIG Migration Winter School in India
0304/20
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RAU Students Participating in the XXII International Competition on IHL
0306/19
The XXII International Humanitarian Law Competition after Friedrich F. Martens took place in Moscow, Russia.
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RAU Researchers' Theoretical Model Experimentally Proven
2301/19
A collaborative research article feauring scholars from Armenia, Russia, Germany and U.S. was published in the journal Nanomaterials.
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PhD Student Aelita Hakobyan Participating in a Diplomatic Seminar
3105/18
The seminar gathered young experts to discuss contemporary issues concerning international relations in economy, trade, politics, culture and national security.
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