2018 IEEE CIS Summer School on Deep Learning and Computational Intelligence


The main idea of this school is to raise awareness and applicability about Deep Learning (DL)
with Computational Intelligence (CI) among the research communities. Traditional learning
and computing methods deal with vast range of applications related to reasoning, decision
making, perception building etc. However, DL with CI deals with dynamical systems more
efficiently by embedding and facilitating learning mechanism. Most of the data obtained in real
time environment from various domains i.e. environment, industry, business, biology involves
lots of imprecision and vagueness. To address these issues in various domains of algorithms,
tools or techniques are required which should be adaptive and robust so that they can handle
the uncertainty and dynamic nature of this system in efficient and optimized way.
“CI is a set of biological and linguistic tools and methodologies to address complex real-world
problems to which traditional Artificial Intelligence (AI) approaches may not be very effective.
CI comprises of concepts and implementations that ensures intelligent behaviour in complex
and dynamic environment.”

According to Robert J. Marks, “Neural Network, Genetic Algorithms, Fuzzy systems,
evolutionary programming and artificial life are the building blocks of Computational
Using CI tools, we will be able to build the systems which are prone to adaption, robust across
problem domains, apply extrapolated reasoning and behave intelligently in given state. Neural
network techniques provide capability of computational adaption. System can improve its
parameter without any intervention based on optimizing criteria same as human learning
occurs. Fuzzy systems help in defining the system where we have a rough estimate of system
requirements. Evolutionary algorithms are good enough to optimize parameters and to select
best among given constraints. Synergistic effect of these tools may increase their individual
performances and gives better adaptive and reliable system. Knowledge representation,
reasoning, information mining, discovery science, web intelligence, semantic web, multi agent
systems and designing of products i.e. air conditioners, automobile systems, ABS, cameras,
dishwashers, pattern recognition in remote sensing, video games are the major areas where CI
can be very helpful.
Advantages of DL and CI over existing deep learning algorithms: One has automatic structure
optimization ability where a neural network structure (e.g., the number of layers, the number
of units in each layer, the type of an activation function at each unit, etc.) is automatically
optimized for a given data set and a given objective by an evolutionary structure learning
technique. The other is multi-objective ability where several different neural networks are
simultaneously obtained under a multi-objective scenario. Various multi-objective
formulations can be considered for deep learning. A general formulation is a combination of
complexity minimization and accuracy maximization. For detection problems, false positive
and false negative can be handled as separate objectives.
The school will bring people working in CI and DL domain to a common platform for
generating innovative ideas. The school will also assess the state of the art on what new
directions lie open for research in area of CI and DL. In this way, the school will generate
exciting new communication across various CI and machine learning disciplines.
The school has attracted around 99 participants (Annexure I) from various engineering
colleges, industries and organizations across the India. In nutshell, the event was an excellent
opportunity where thought-provoking lectures were conducted for fruitful interaction and
several technical challenges. It helped exciting new communication across various DL and CI
disciplines and helps to define an emerging international research community.

  Date and Time




  • Start time: 05 Dec 2018 09:00 AM
  • End time: 07 Dec 2018 05:00 PM
  • All times are (UTC+05:30) Chennai
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  • Kanpur, Uttar Pradesh
  • India 208016