Name: GRADnet Machine learning and AI workshop
Start: 2020-01-15T09:00:00+00:00
End: 2020-01-16T17:15:00+00:00
Location: Arts II

GRADnet Machine learning and AI workshop

from Wednesday 15 January 2020 (09:00) to Thursday 16 January 2020 (17:15)

Monday 13 January 2020
Tuesday 14 January 2020
Wednesday 15 January 2020

10:30 Break
Break
10:30 - 11:00
Room: LT
11:00
11:00 - 12:30
Room: LT
Contributions

11:00 Welcome and housekeeping - A Bevan (Queen Mary)

11:10 Why is data wrangling important? - Nick Barlow (Alan Turing Institute)

11:30 Data wrangling exercises - Nick Barlow (Alan Turing Institute)
12:30 Lunch
Lunch
12:30 - 13:30
Room: LT
13:30
13:30 - 15:00
Room: LT
Contributions

13:30 Introduction to Python and TensorFlow (linear regression and neural network examples) - A Bevan (Queen Mary) JMM Davies (Queen Mary) Marcella Bona (QMUL) TP Charman (Queen Mary)
15:00 Break
Break
15:00 - 15:30
Room: LT
15:30 Plenary
Plenary
15:30 - 17:00
Room: LT
Contributions

15:30 Reinforcement learning - A Pourtsidou (Queen Mary)

16:15 Sci ML team @ RAL - Jeyan Thiyagalingam (STFC)
18:00 Dinner
Dinner
18:00 - 20:00
Room: LT
Thursday 16 January 2020
09:00
09:00 - 10:30
Room: LT
Contributions

09:00 Linear regression, neural network model building, and decision trees (tutorial using SciKit Learn and Keras) - A Bevan (Queen Mary) JMM Davies (Queen Mary) Marcella Bona (QMUL) TP Charman (Queen Mary)
10:30 Break
Break
10:30 - 11:00
Room: LT
11:00
11:00 - 12:30
Room: LT
Contributions

11:00 NNPDF - using neural networks to compute parton density functions - Maria Ubiali (DAMPT)

11:45 ABBA - Automatically Building Better Algorithms. - John Woodward (QMUL)
12:30 Lunch
Lunch
12:30 - 13:30
Room: LT
13:30
13:30 - 15:00
Room: LT
Contributions

13:30 SQL tutorial - A Pourtsidou (Queen Mary)

14:15 Data in finance - Thomas Babbedge (Gresham Investment Management LLC)
15:00 Break
Break
15:00 - 15:30
Room: LT
15:30
15:30 - 17:15
Room: LT
Contributions

15:30 Challenges of Machine Learning in Physics - A Bevan (Queen Mary)

16:15 Machine Learning applied to neutrino physics - Sophie King (Kings College London)

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