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Introducing UTMIST!

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

  2. A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

                                                                                I, Robot, by Issac Asimov


In the past we used to think that artificial intelligence only existed within scientific fictions, in the wild imagination and fantasy of their authors.

However, the success of Alpha Go has completely upended our thinking in this regard and showed us: Artificial Intelligence has permeated our daily lives!

How much do you really know about artificial intelligence, though?

Let UTMIST tell you about it all!


We are UTMIST

The University of Toronto, as one of the top education institutes in the world, leads the way in the field of artificial intelligence, and it has Geoffrey Hinton’s Machine Learning Group – the first of the three world-leading forces in the field of deep learning. However, undergraduate students seldom have the opportunity of getting in touch with the scholars from the Machine Learning Group, or getting exposure to leading technology in machine learning. This has created a disconnection between the undergraduate students and top scholars in machine learning. UTMIST aims to clear the mist by demystifying artificial intelligence technology, which is the most trendy technology nowadays. As a student organization focused on machine learning research, UTMIST is to establish a close connection between the undergraduate students and top academic resources at U of T.

University of Toronto Machine Intelligence Student Team (UTMIST) is an officially certified student organization within the University of Toronto. Our mission is to let more people get to know about artificial intelligence, “Clear the mist”!

 


In order to help everyone learn machine learning in a systematic way, UTMIST here presents our carefully prepared workshop series – “MIST101”

MIST101

During the initial stages, UTMIST will host a series of workshops on machine learning and data science – “MIST101”, and show everyone the most advanced, popular and upfront technology.

The workshop will be hosted on a bi-weekly basis, and the content will be prepared by our outstanding academic team. The workshop will cover supervised learning, unsupervised learning, TensorFlow and more. We will start from the basic theories and extend to the most cutting-edge research. Our instructors will lead everyone from the basics into the deeper understanding of what machine learning is and how it is to be applied in real life. You will get the opportunity to interact with our instructors, getting face-to-face guidance, and there may even be an opportunity for you to join our instructor’s research project!

UTMIST welcomes all undergraduate students who are interested in machine learning. Even if you are not planning to pursue a career in machine learning, everything you learn from MIST101 will still give you a head start in interacting with the field in artificial intelligence!


MIST Academy

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Sheldon Huang

  • UTMIST President and co-founder.
  • Recent Research in the Machine Learning Group at U of T: Generative Adversarial Network, Optimization, Style and Domain Transfer.
  • TA in the Mathematics Department at University of Toronto. He was a calculus TA in his second year, and a linear algebra TA in his third year.
  • Started his research career in Grade 9. Some of his other researches include: real analysis, non-linear optics and life sciences. Published his first paper during the summer after Grade 10.
  • Currently a 3rd year Computer Science student at University of Toronto.  

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Justin Yuan

  • UTMIST co-founder and project director.
  • Third year undergraduate student; Bachelor of Engineering Science Candidate (Robotics Major).
  • 2017 Cansat Competition U of T Team, program team lead.
  • Research intern at the D3M lab; focuses include image classification, text classification and attention mechanism in deep learning.
  • Current IEEE UofT student branch, event director of computer chapter.
  • University of Toronto application development association senior developer.
  • Technical analyst at the renowned media company Synced Review (global).

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Colin Li

  • UTMIST co-founder and project director.
  • Third year undergraduate student; Bachelor of Engineering Science Candidate (Robotics Major).
  • CSC373 (Algorithm Design, Analysis and Complexity) TA.
  • 2017 ACM NA regional competition and 2017 NAIPC competition top 10
  • Participated in the research project of using deep learning techniques to improve the trajectory tracking performance of quadrotors.
  • Published research paper at IEEE international robotics and automation conference.
  • Lead author of the published paper – “ Deep Neural Network for Improved, Impromptu Trajectory Tracking of Quadrotors” – Q. Li, J.Qian, Z.Zhu, X.Bao, M.K.Helwa and A.P. Schoellig.

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Xuechen Li

  • UTMIST project director.
  • Third year undergraduate student major in computer science, mathematics and statistics.
  • Summer research project: Bayesian neural network and variational inference.

Talk series

If MIST101 leaves you curious for more knowledge in machine learning, then our talk series will be your next step!

UTMIST will host research talks regularly, and we will have the most influential scholars as speakers to share their research results. The guest speakers to be invited will include graduate students, current technology company employees and professors. They will explain and analyze the most cutting-edge technology of machine learning from their own perspectives, and share their thoughts about the future trends in the field. Approaching the subject from various perspectives, our talk series will be able to help the attendants have a grasp of the latest development in machine learning in the most comprehensive way.

Most importantly, all our talk series are free and open to the public!


With our mission in mind, UTMIST appreciates all your support and aspires to present you with the best and most exciting contents!