Big Data School

About this course

Description
Teachers

Description

About this course

Intended for doctoral students, master’s students, researchers and industry professionals aiming to deepen their knowledge and skills in the field of big data. Participants will explore advanced technologies, work with real datasets and enhance their understanding of neural networks, time series analysis, predictive models and AI applications in finance. The course emphasises the practical application of theoretical knowledge, hands-on training in Python and development of analytical competencies. Training sessions are delivered in collaboration with field experts.

You will learn:

to work with large datasets and apply interpretation techniques;
to differentiate between feedforward, recurrent and LSTM neural networks;
to apply RNNs for sentiment analysis, question answering and text generation;
to use the Python programming language for time series analysis;
to classify data using the support vector machine method.

Have questions?

Kotkevičienė Alina

phone +370 37 300 303
email alina.kotkeviciene@ktu.lt

Start date: Organized upon forming a group

Duration: 24 ac. hrs.

Language: English

Price: 350 Eur

Method of organisation: On-campus learning

Skill area: Digital transformation

Certificate: Issued

Teachers

Kavaliauskas Mindaugas

Dr. Mindaugas Kavaliauskas (KTU Faculty of Mathematics and Natural Sciences, Lithuania) – received his degree of Ph.D. in Mathematics (2005). He is currently associate professor at KTU. He is giving lectures on mathematical statistics, time series analysis, and data mining. His fields of scientific interests are multivariate data analysis, statistical models, applied mathematics, and machine learning.

Iešmantas Tomas

Dr. Tomas Iešmantas (KTU Faculty of Mathematics and Natural Sciences, Lithuania) – is currently associate professor at KTU. His main research areas are application of deep learning methods in medical diagnostics, as well as using AI-based methods for various industry applications. The researcher teaches graduate students Mathematical Methods of Artificial Intelligence, Data Analysis, Data Mining Methods, Machine Learning Methods and Programming for Data Processing and Visualization.

Fairbank Michael

Dr. Michael Fairbank (University of Essex, UK) – is a Computer Science lecturer at the University of Essex, UK. He is an active machine-learning researcher, with publications in reinforcement learning, deep learning and neural networks. In his previous careers he worked as a computer consultant and as a mathematics teacher. He has a passion for all things related to computing, mathematics and AI.

Meert Wannes

Dr. Wannes Meert (KU Leuven, Belgium) – received his degrees of Master of Electrotechnical Engineering, Micro-electronics (2005), Master of Artificial Intelligence (2006) and Ph.D. in Computer Science (2011) from KU Leuven. He is currently research manager in the DTAI research group at KU Leuven. His work is focused on applying machine learning, artificial intelligence and anomaly.