Deadline for Full Paper Submission : July 7th, 2019
Announcement of Accepted Papers: August 23th, 2019
Deadline for Camera Ready Papers & Early bird registration: September 13th, 2019
Conference: October 31st – November 2nd 2019
The Adventure of Machine Learning
One of the fields of computer science is to develop thinking, learning and problem solving systems. Although our efforts in this area sometimes result in great disappointments, we do not give up. We are trying to develop more intelligent and resourceful machines. Here, Machine Learning is the adventure of mankind's racing his own intelligence with his own machines.
Machine Learning has recently been transformed into a new area called Deep Learning. Scientists who research in the field of Deep Learning model these processes by studying the cognitive processes of living things and develop algorithms that can learn using these models.In this speech, the process of machine learning to deep learning will be explained and the theoretical and technological end point of this field.In addition, the philosophical, sociological and ethical problems of Deep Learning technologies will be discussed.
Subspace Methods and Finance Applications
Subspace methods spanning from principal component analysis (PCA) and Karhunen-Loeve transform (KLT) to wavelet transform have been successfully used in applications including face recognition, facial emotion analysis, image/video compression, recommending systems and many others. In this talk, we will revisit the mathematical concepts underlying these powerful analytical tools and emphasize a unified perspective for their use in signal decomposition and time-frequency analysis. Then, we will focus on eigensubspace of normalized returns for a basket of assets and present the methodology to design their eigenportfolios for financial investments. We will present and compare performances of Minimum Variance, Market (Markowitz) and Eigen Portfolios for US Equities to highlight the merit of the latter.