Time | 30th July | 31st July | 1st August | 2nd August | 3rd August |
---|---|---|---|---|---|
8:00-9:15
9:15-9:30 |
Keynote 2:
Democratizing AI in Developing Countries
(SM)
Short Break |
Talk 6:
Applications of
Deep Learning on
multi-scale physics
based simulators
(JD)
Short Break |
Talk 10: Deep
Learning for video
classification:
Application and state
of the art approach
(SRS)
Short Break |
||
9:30-10:45
10:45-11:00 |
Inaguration
|
Networking + project discussion (for participants)
|
Keynote 3 : Robust
Data Mining for not
to be misled by
Numbers
(SA)
Short Break |
Networking + project discussion (for participants)
|
Networking + project discussion (for participants)
|
11:00 -12:15
12:15 - 12:30 |
Keynote 1:
Introduction to
Machine Learning
(SRJ)
Short Break |
Talk 3: Artificial
intelligence in
Education 4.0
during/after
Pandemic (MP)
Short Break |
Talk 7: Shallow
and deep learners
for tabular dataset
(RK)
Short Break |
Poster presentation
|
Panel Discussion
|
12:30 -13:00
13:00 - 13:45 |
Sponsor Time
Lunch Break |
Sponsor Time
Lunch Break |
Sponsor Time
Lunch Break |
Sponsor Time
Lunch Break |
Sponsor Time
Lunch Break |
13:45 - 15:00
15:00 - 15:15 |
Talk 1: Supervised
Machine Learning
pipeline: Step by
step Tutorial (TBS)
Short Break |
Talk 4: Effective
Approaches and
Machine Learning
Algorithms to deal
with time series
data (JP)
Short Break |
Talk 8: Machine
Learning approach
to solving natural
language
processing
problems (BKB)
Short Break |
Talk 11: Generative
Adversarial Network
(BB)
Short Break |
Talk 13: Methods
used for the
development of deep
learning for remote
sensing applications
(BM)
Short Break |
15:00-16:30
|
Talk 2: Application
of Machine Vision
and Deep learning
in Agriculture (AK)
|
Talk 5: On
managing data
science artifacts
(RS)
|
Talk 9: Practical
Reinforcement
Learning (AP)
|
Talk 12: Big Data
Processing with
Pyspark (BG)
|
Talk 14: Causal
Modelling (DR)
|
16:30-18:00
|
Project session
(for participants)
|
Project session
(for participants)
|
Project session
(for participants)
|
Project session
(for participants)
|
Closing Session
|
In this project the idea is to implement 3 Sentiment Analysis models using LSTM, GRU and also CNN and compare accuracies.
Data source:
We create a deep learning system using OpenCV and Keras/PyTorch/Tensorflow to determine if masks are worn or not.
Reference article: https://www.pyimagesearch.com/2020/05/04/covid-19-face-mask-detector-with-opencv-keras-tensorflow-and-deep-learning/