On 21 May, the third webinar in the cycle Fundamentals of Machine Learning, Machine learning project presentations by each participant and discussions, will take place at Rīga Stradiņš University (RSU).
Preliminary presentation content: description of the selected problem and data set, preparation of data set for training machine learning algorithms, visualizations of dataset, selection of the best machine learning algorithm to achieve maximum performance (comparison of at least three different algorithms), hyperparameter tuning and conclusions.
About the instructor
Uldis Doniņš is the Head of the Information Systems Unit of the IT Department at RSU. He holds a PhD (Dr.sc.ing.) in Computer Science and his field of study is software modeling and modeling formalisation. Uldis has expanded his knowledge and experience in the fields of machine learning and data intensive computing at the University at Buffalo (State University of New York, USA), School of Engineering and Applied Sciences. Being a part of Artificial Intelligence Machine Learning provides computer learning and decision-making based on the provided data that can be developed using supervised, unsupervised or reinforcement learning models. Data intensive computing deals with diverse data formats, storage models, application architectures, programming models and algorithms and tools for large-scale data analytics.
- About the webinar cycle
As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Being a part of Artificial Intelligence, Machine Learning provides computer learning and decision-making based on the provided data. Seminar is intended to provide insight into Machine Learning and its algorithms covering supervised and unsupervised learning, including data processing and application for machine learning solutions. Participants will get hands-on experience in implementing machine learning solutions by using Python which currently is one of the most popular programming languages.
The practical part is based on individual work on implementing machine learning project by using Python.