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MSc AI/Data Science

MSc in AI / Data Science
To foster mathematically trained AI experts

To raise the most rigorously trained reseach scientists in AI/Data Science

MSc in Artificial Intelligence and Data Science – MSc AI/DS 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 required to pass the admission examination with over 60% performacne. The exam will be similar to final exams of last two courses in PreMSc - STA511 and STA512

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MSc in AI/DS

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

MSC-Tabs

Program Structure
How classes work
  • 3-hour Video-recorded classes per week (Required)
  • 1-hour support sessions per week  (Selective)
  • Final exam / term paper 1-week after the end of the course
  • Total 12 courses, 2 courses for 1 term
  • 1 term for 8 weeks
Course Curriculum
Course Curriculum

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

Core Course Description

Admission Examination
Admission Examination

Admission examinations are administered by GIAI

  • Coverage: Two final courses in Pre-MSc
    – Sample course materials: STA511, STA512
  • Support course & exam
    – Registration: SIAI MSc Admission Exam | GIAI
    – Course fee: CHF 800
    – Exam date: 2nd weekend of July
    – More details to refer SIAI MSc Admission Exam | GIAI
  • Grading
    – 70% or above: Scholarship, RA/TA opportunities
    – 60% or above: Pass
    – Below 60%: Fail
Admission
Admission

Admission examination

  • 60%+ in admission exam
  • or Pre-MSc AI/Data Science 1st year’s 60%+ (Specifically STA511 and STA512)
  • 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

 

Tuition Fees
Tuition Fees
  • Application fee : CHF 200.- (Non-refundable)
  • Administration fee : CHF 1,000.- (Non-refundable)
  • Courses : CHF 1,650 per course
    – 2 courses per term (Bi-monthly payment)
    – 1 course for 5 ECTS*

Graduation requirements

  • Coursework – 60 ECTS* wth average 60% or above
  • Dissertation – 30 ECTS*
    • 20,000 words academic essay or equivalent mathematical/programing application
    • 6 months support course (CHF 2,000)

*ECTS – European Credit Transfer and Accumulation System

Scholarship

  • If 70% or above in admission examination
  • RA/TA opportunities
English requirement
or non-native English speakers, should meet one of the following criteria

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 iBT 100/120 or above (with each section at least 21/30)
  • IELTS 7 or above (with each section at least 6.5/9)
  • Pass grade from SIAI’s internal English course

 

Internal English course

  • Course fee: CHF800
  • Course schedule
    • July~Aug (8 weeks)
    • Live session
    • 3 hours per week (usually weekend)

Q&A

Please note that it is our flagship program that we only look for highly trained students in Mathematics, Statistics, and critical reasoning, along with above-average programming skills sets. Example student types that we look for are PhD in Mathematics, Physics or any STEM disciplines that do not help them to be market ready for AI/Data Science jobs. Survival rates without such dedicated training have been the lowest in every academic year.
Students must earn 50% or above on average in coursework and should pass dissertation. Grading scale of the school is available from school's regulation. Students must finish the coursework within two years and should pass dissertation in the following year
Students can choose to write dissertation during the 2nd semester or after the coursework. If to finish the dissertation by the end of 2nd semester, the graduation will be September of the year, otherwise September of the following year. The dissertation support course offers total 6 meetings with students. Students are to meet instructors via online meeting and all conversations will be recorded for personal and peer review purposes.
We do not expect students to be software engineers. We want students to be a data/research scientist with scientific programming skills. How fast you type codelines or how long code have you debugged, for example, are irrelevant experience for us. We want students to convert mathematical reasoning to program code to materialize thought experiments. Most courses provide sample codes for assignments and examinations.
In fact, unless a student has to pursue an academic career for rigorous studies, we tend to discourage them to 'torture' oneself. Since required skill sets for AI/Data Science jobs are mostly covered by MBA AI programs, even with PhD in Math, for example, marginal gain from MSc is small. MBA graduates re-join the school after a few years of AI job for later MSc courses and switch the diploma from MBA to MSc, and we believe this is far more efficient and smarter strategy.
AI Engineers from technologically 2nd-tier institutions think running AI code libraries is AI/Data Science. We want our students to pre-check the data to identify potential correlations that may affect denseNet's performance, thus propose a revised model to either re-design the NeuralNet or change the data structure accordingly. At the end of the day, all good models are DGP (Data-Generating-Process) optimised variations of existing models, not a copy of other company's working code. This is our version of 'Critical Thinking'
Computer programming indeed is essential in every sub-discipline at SIAI, but students are given ample amount of study materials. For starters, Issues in Computer Programming (OPT101) , a short prep course given to all incoming students, covers a number of programming issues that are basics for later courses. In addition to that, for every problem set and term paper, a set of guideline code lines are given so as to provide right direction.
Unfortunately, as an online business school, we do not provide any offline support for learning. However, students are free to ask to the course forum. Professors and TAs are available within a short reach of forum posting.

SIAI Experience Stories 

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.

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.

MSc AI/DS is our flagship program.
One day it will be your flagship.
Course Curriculum
Department Contact Info

Master of Science Program

[email protected]

Mon – Fri 9:00 A.M. – 6:00 P.M.

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