welcome to machine learning for
engineering and science applications
I am Balaji Srinivasan I am in the
mechanical engineering department
hi I'm gonna put the Krishna mood I am
in the department of engineering design
and both of us are from iit madras so if
we look at various applications that all
of us are using already in real life for
example this is Amazon's recommender
system some of you might have seen
Amazon echo which is a speech
recognition system of course everybody
has used Gmail spam classifier and this
is Google Lexus this is a latest
self-driving car
all of these use machine learning
algorithms in one way or the other
our purpose in this course is to try and
utilize the same algorithms for more
general problems for example medical
image diagnosis or for speeding up CFD
computations we look at the course aims
basically we will try to understand some
of the basic machine normal learning
models thoroughly with specific emphasis
on deep learning which is the current
state-of-the-art in machine learning for
us since recent advances in cans or
generated vessel networks and also in
reinforcement learning we will learn to
apply these techniques to problems in
engineering for example problems in
medical image analysis as well as
turbulence modeling in computational
fluid dynamics we expect you to learn to
program in Python and also to learn to
program in popular deep learning
platforms like packet arch and tensor
flow will feel the course auckland the
course consists of three broad parts the
part one will be focused on artificial
neural networks and deep learning or
deep neural networks which will include
CN NS and or enhance or recurrent neural
networks and other T will also cover
other classical techniques like binary
decision tree Sandom forest and
probabilistic techniques we will
conclude the course with with a look at
the recent advances in the fields of
deep learning which includes
variational autoencoders auto-encoders
in general generative models which
include generality of additional
networks as well as a small introduction
to reinforcement learning
thank you
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