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Jasmina Djordjevic is an associate professor at the Department of Mathematics, Faculty of Sciences and Mathematics, University of Nis. Her main fields of interest are backward stochastic differential equations, stochastic control, time change processes, application in epidemiology & finance, and data, and risk analysis. She is into applications, so her interest in ML came naturally.
Jasmina took part in the research group Stochastic & Risk at the University of Oslo, Norway, as well as in multi-scientific and interdisciplinary projects. She has published more than twenty papers, written two books and the third one is on the way.

This topic will cover the intro to machine learning and algorithms. Machine learning is an artificial intelligence discipline that skyrocketed over the years. It involves the creation of algorithms and statistical models that allow computers to improve their performance in tasks via experience. These models and algorithms are created to learn from data and generate predictions or assumptions without specific instructions.
Analysis and prediction with different types of ML algorithms are an endless playground with a bouquet of possibilities. Different problems, sets of information, and gains draw different approaches and results.
Jasmina will review what are the aims, paths & possibilities of ML algorithms during the event.
At Diligent, we constantly expand our expertise and keep in touch with the latest cutting-edge technologies to encourage the fulfillment of our potential.
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CTO of GBI, Peter Custer, reflects on 14 years with Diligent—trust, alignment, and teamwork driving impactful projects and lasting success.
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Traditional models like ARIMA and Exponential Smoothing have been reliable for certain contexts, but the advent of deep learning has shifted the landscape, allowing for the capture of more intricate patterns. However, these advanced models often face challenges related to complexity and efficiency. Enter SOFTS, a revolutionary approach designed to tackle these challenges head-on. SOFTS, short for Series-cOre Fused Time Series forecaster, is an efficient MLP-based model that sets a new standard in multivariate time series forecasting.
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