Quality of Education

Introduction

Academic training is not so much memorizing knowledge as it is to build a frame of thinking and to apply the frame to analyze the real world problems. A substantial number of ill-concept education bodies disregard the true purpose of academic study and focus only on short-sighted job training. SIAI is designed to provide purpose of academic education as our primary goal. As we grow, we target to restore AI education industry with long-term view in training and sideline disqualified diploma mills.

Education is not a factory process to generate complete material, but a delivery process to envision right goals in study and to strengthen basis for the next level challenges. SIAI is to help students in learning ground knowledge that can support advanced and applied studies for business applications of computational science. With ample experience in class, we believe students will be well-versed for a variety of applications of AI and Data Science for every new challenges.

The Foundations of Quality of Education

Mission

True nature of Artificial Intelligence (AI) is not so much engineering as it is computational science that is fundamentally based on advanced mathematics and statistics. SIAI’s mission is to propagate the real-self of AI to not-so-technically trained business people. In addition to that, in the era of big data, which is defined not by the size of the data, but by the multi-patterness and behavior stream embedded in serial sequence data, SIAI dreams to train well-versed work force whose core expertise is to transform advanced math, stat into logical analysis of such data.

Vision

The misconception created by engineers that AI is all about engineering has to be questioned, challenged, and revised by statistically trained researchers whose training is oriented to apply the data science knowledge to the real world.

SIAI believes that providing such training to global scale by online higher education will facilitate the pertinent applications of data science and eradicate over-emphasized dependency on computationally heavy models, such as Deep Learning. As more and more work force understands invalidity of applying deep neural network models for statistically challenging questions, the lower quality engineers will be sidelined from the field of data science. We hope to add one more edge to such stream of developments

Quality Education Principles

SIAI’s two principles of quality education are

  • Higher education that is based on basic training
  • Applied education by the abstracted use of basic science

Higher education in advanced studies often too doctrinated in theoretic aspects of scientific studies, which makes students unable to utilize the learned concept to the real world. SIAI’s courses are consist of quality problem sets that are abstracted and simplified versions of edge research in data science. On the way of problem solving in classes, students can train themselves in ways of

  • Logical thinking upto high quality papers
  • Academic training to real world applications
  • Intuitive applications of abstract concepts