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Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several parameters.The conference aims to focus on gaining theoretical insights in the computation and setting of these parameters. The organizers solicit papers that focus on exploring alternatives to gradient descent/ascent types methods. We would welcome cutting-edge research on aspects of deep learning theory used in the fields of artificial intelligence, statistics and data science, theoretical and numerical optimization. Astronomy is a fascinating case study as it had embraced big data embellished by many sky-surveys. The variety and complexity of the data sets at different wavelengths, cadences etc. imply that modeling, computational intelligence methods and machine learning need to be exploited to understand astronomy. The inter-disciplinary study of astroinformatics brings together machine learning theorists, astronomers, mathematicians and computer scientists underpinning the importance of machine learning algorithms and data analytic techniques. The Conference aims to set a unique ground as an amalgamation of the diverse ideas and techniques while staying true to the baseline. We expect to discuss new developments in modeling, machine learning, design of complex computer experiments and data analytic techniques which can be used in areas beyond astronomical data analysis. The meeting is sponsored by: Department of Computer Science and Engineering & Center for AstroInformatics, Modeling and Simulation, PES University; IEEE Computer Society Bangalore Chapter; and International Astrostatistics Association.