Corrigendum: Joint STI - ICTS Seminar IN PERSON: Wednesday, 19 October 2022 at 3 pm

[STI Secretariat]

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Joint STI - ICTS seminar
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You are most cordially invited to a seminar jointly organized by the 
ICTP Science Technology and Innovation Unit (STI) and the Information 
and Communication Technology Section (ICTS) on

** * * Wednesday, 19 October 2022 at 15:00 *** * *

*Venue: Fibonacci Lecture Room, ICTP Galileo Guesthouse, entrance level

Speaker: *Nurfadhlina Mohd Sharef,* Universiti Putra Malaysia

Title:***INTERACTIVE MACHINE LEARNING WITH DEEP REINFORCEMENT LEARNING 
AND GENERATIVE ADVERSARIAL NETWORK IN DIGITAL TWIN*

Interactive machine learning (IML) is also known as human-in-the-loop 
(HITL), and is an increasingly important area in future research due to 
the fact that knowledge learned by machine learning cannot win human 
domain knowledge. For example, traditional ML methods are inefficient in 
handling small datasets or complex datasets, whereas IML approaches are 
effective for handling this problem. IML can support machine learning 
modeling in small data and allows humans intervention to improve the 
modeling, and the model to continuously learn by the interactions. The 
main goal is to design adaptive agents that support meaningful and 
beneficial interaction with humans. However, how the ML mechanism, 
well-defined on statistical assumptions,  work on practical data, and 
how is the ML model updated in each iteration according to input 
features, are not transparent and usually ignored. Especially when 
applied to specific applications, it is essential to study when and why 
the ML algorithms work better or worse than expected, and then adjust 
the model accordingly. This indicates the need of a transparent ML tool 
that can clearly show the learning process to users and can 
significantly facilitate the exploration of ML models. On the other 
hand, burdening human users for tuning the ML model may be complicated. 
Therefore, an IML framework that utilizes deep reinforcement learning 
and generative adversarial network methods in the DT are needed to 
maximize a reward function in optimising the ML models, whilst including 
human interaction for semi-autonomous decision making. This talk will 
share findings about deep reinforcement applied in several digital twin 
environments, such as recommender system, student grade prediction, 
biodiversity and supply chain.

-- 
Nicoletta Ivanissevich (Ms.)
Secretariat,
STI - Science, Technology and Innovation Unit
The Abdus Salam International Centre for Theoretical Physics
Strada Costiera, 11
I-34151 Trieste
Italy

Phone: +39-040-2240.383
STI Webpage:https://www.ictp.it/research/sti.aspx
Email:sti at ictp.it

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