The Machine Learning Hub

ML @ KAUST

Tutorial Dates

  • 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. 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.
    Course 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