Tutorial Dates

• January 27 2020, Bldg. 19 Hall 1
  Amazon Web Services (AWS) ML Immersion Day

  Overview: As part of the ML Hub education series, we offer a full-day immersion tutorial and hands-on lab on Amazon's ML tools.
  Necessary equipment: All attendees must bring their laptop and have an Internet access.
  Registration: required. Please register using this form.
  Schedule:
  

  08:30-08:45	Welcome and Introductions
  08:45-09:15	Customer Overview – Discussion on Projects, Use Cases, Workloads, Needs
  09:15-10:15	Module 1 – Introductions to AWS AI and machine learning services
  10:15-10:30	Break
  10:30-12:00	Module 2: Hands-On Lab – Building a chat bot using Amazon Lex
  12:00-13:00	Lunch
  13:00-14:00	Module 3 – Introduction to Amazon SageMaker
  14:00-16:00	Module 4: Hands-On Lab – Amazon SageMaker Workshop
  16:00-16:15	Close-out, Next Steps, and Goodbye

  Workshop presenters: Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS).

• April 24-25 2019, Auditorium 0215 (between Bldg. 4 and 5)
  A Short Course on Deep Learning and the Latest AI Algorithms by Prof Xavier Bresson (NTU Singapore)

  Overview: The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises.
  Course schedule: The course takes place in Auditorium 0215 on April 24-25. from 9:00. The detailed schedule and covered topics are available at the course website.
  Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python.
  Necessary equipment: Participants must bring their laptop to run the Python notebooks (no Python installation required as the notebooks run on the Cloud).
  Course registration: If you are interested to register, please fill up the following form. Note, that this course has limited seating and filling this form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.
  Schedule:
  Day 1 - April 24 2019

  9:00-10:30	L01-Introduction to deep learning  L02-Linear algebra and Pytorch
  10:30-10:45	Break
  10:45-12:15	L03–Vanilla neural networks part 1
  12:15-13:15	Lunch
  13:15-14:45	L04–Vanilla neural networks part 2
  14:45-15:00	Break
  15:00-16:30	L05–Multi-layer perceptron part 1
  16:30-16:45	Break
  16:45-18:15	L06-Multi-layer perceptron part 2

  
  Day 2 - April 25 2019

  9:00-10:30	L07-Convolutional neural networks part 1
  10:30-10:45	Break
  10:45-12:15	L08-Convolutional neural networks part 2
  12:15-13:15	Lunch
  13:15-14:45	L09–Recurrent neural networks part 1
  14:45-15:00	Break
  15:00-16:30	L10–Recurrent neural networks part 2
  16:30-16:45	Break
  16:45-17:15	L11- Recurrent neural networks part 3

  Bio: Xavier Bresson received his PhD in 2005 at the EPFL in Switzerland. He is an Associate Professor in Computer Science and member of the Data Science and AI Research Centre at Nanyang Technological University (NTU) Singapore. His research interests are in the field of graph deep learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains in neuroscience, genetics, social science, physics, and natural language processing. In 2016, he received the highly competitive Singaporean NRF Fellowship of $2.5M to develop these new deep learning techniques. He was also awarded several research grants in the U.S. and Hong Kong. As a leading researcher in the field, he has published more than 60 peer-reviewed papers in the leading journals and conference proceedings in machine learning, including articles in NIPS, ICML, ICLR, JMLR. He has organized several international workshops and tutorials on deep learning in collaboration with Facebook, NYU, and USI such as the 2018 IPAM workshop, the 2017 CVPR tutorial and the 2017 NIPS tutorial.