MSc AI/DS (1 year)
MSc in Artificial Intelligence / Data Science
To foster mathematically trained AI experts
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MSc in Artificial Intelligence and Data Science (1 year) – MSc AI/DS (1 year) program is a master level training for computational science, an academic discipline known as Artificial Intelligence. The essence of the program lies in deep mathematical and statistical modeling that requires heavy computational support. The math topics are graph theory, information theory, game theory along with statistics in panel data, computational Bayesian and recent developments in advanced machine learning techniques.
Unlike other institutions, SIAI provides AI educations in the context of business applications. We have organized cross-discipline projects with engineering and medical departments for self-driving module’s defensive driving and drug effectiveness through virtual human simulator. Both of which projects require not only aforementioned math, stat, and computational scientific hard skills but also practical business applications that every breaking new technology needs.
For the admission of the program, students are given to pass an examination. The sample of the exam is provided on our blog (link).
Lecture Note
MSc in AI/DS (1 Year)
at a glance
HIGH QUALITY ONLINE CLASSES
ONLINE TA SESSIONS
12 COURSES & 1 DISSERTATION
8 WEEKS PER COURSE
2 COURSES PER WEEK
1 YEAR PROGRAM
Learning Outcomes
APPLICATION OF ABSTRACT MATHEMATICAL CONCEPTS TO REAL WORLD PROBLEMS
MATHEMATICAL AND BAYESIAN STATISTICS, DYNAMIC OPTIMIZATION FOR UPPER LEVEL STUDY
How classes work
- Online live/recorded classes from 7pm-10pm on weekdays (Required)
– Mostly Mon-Wed, only cross-program courses on Thurs - Online live TA sessions on weekends (Selective)
– Mostly 1-2 hours in day time on Sat - Final exam 1-week after the end of the course
- Total 12 courses, 2 courses for 1 term
- 1 term for 8 weeks
One 5 ECTS* course is consisted of 8 lectures, 7 TA sessions, and 1 final exam. Total teaching hour is 33-40 hours and together with self-study hour, per course required hour is 125.
Admission
- Entrance exam qualifier with undergraduate diploma
- or MSc AI/Data Science 1st year’s GPA B- or above
- or Approval by program director
Class module : Online only
Credit : 90 ECTS / (Level / EQF 7)
Required documents
- Bachelor diploma and transcript (mandatory)
- Graduate school diploma and transcript (if applicable)
- Statement of Purpose
- Curriculum Vitae
For non-native English speakers, should meet one of the following criteria by graduation
- High school or University level diploma from an all-English program
- TOEFL 100 or above (with each section at least 21/30)
- B- or above from SIAI’s internal English course
– Tuition fee of the course is TBD
title
1 Year for 12 courses
1 Term for 8 weeks with 2 courses
Prep classes are available
- LaTeX for assignments and paper writing
- Programming prep for Python
title
- Application fee: CHF 200.- (Non-refundable)
- Administration fee: CHF 1,000.- (Non-refundable, exemption for MSc AI/DS 2 year)
- Courses : CHF 1,500
– 2 courses per term
– 1 course for 5 ECTS*
Graduation requirements
- Coursework – 60 ECTS*
- MSc Dissertation – 30 ECTS*
*ECTS – European Credit Transfer and Accumulation System
title
Admission examinations are administered by SIAI Extension School
- Application fee : CHF 200.- (Non-refundable)
– Technical track: Basic linear algebra, differential equations, introduction to statistics
– Business track: High school math/stat - Examination schedule
– Available on SIAI Extension School
– Technical track: CHF 50 per exam
– Business track: Free - Change of program
– Tech → Biz: No penalty
– Biz → Tech: Re-evaluation required
For non-native English speakers, should meet one of the following criteria
- High school or University level diploma from an all-English program
- TOEFL 100 or above (with every section at least 20/30)
- Pass grade from SIAI Extension School’s English course
– Tuition fee : CHF 1,000
Global top quality
What is known as Artificial Intelligence (AI) is a business application of computational science. For that, every course is dedicated for deeper mathematical and statistical theory that are basis for a number of computational disciplines. The curriculum is designed to expand coverage in a variety of computational studies, all of which meet for the ultimate goal of this program. Understanding AI scientifically so that it can be applicable to real world problems.
Every non-linear modeling is accessed in comparison of traditional statistical approaches and recently highlighted machine learning models in order for students to understand computationally heavy models are not always the most relevant choice of AI applications. In this context, the program emphasizes statistical model-based approaches, such as Generalized Method of Moments and Maximum Likelihood Estimation, in the earlier courses.
In term 3 to 5, the curriculum centers on the relevant use of deep neural network and reinforcement learning, which are nothing more than nested non-linear factor analysis and multi-stage dynamic optimization. The coursework concludes by extension of such models to multi-agent cases, one of which is applied to a project for self-driving algorithm’s defensive driving strategy, in the context of game theory and Bayesian statistics.
Upon graduation, students will be able to learn not only the necessary mathematical and statistical theory in the background of AI models, but they are also capable of applying such advanced knowledge into real world businesses.
Fundamental Math and Stat
are critical for “Real World Applications”
in every quantitative discipline
Target for Research Institutions
AI is not about programming, as often argued by many engineering departments. It is all about how to leverage mathematical modeling and statistical intuition in a way to best use of computational science. Our course projects will help you learning them by doing them.are critical for “Real World Applications”
As students absorb SIAI style training and apply it to projects, students will gradually become knowledgeable in which computational strategy to choose for what problem in every context, for which literature to review for deepening their understanding of models, and by which technique a problem can be solved.
We are not a vocational program for practicing coding language, but an academic program that help students to apply hard theory to change our daily life.
Mathematics is only a hobby, if one doesn’t know where to apply. We make math not a hobby.
