On 16 October, the first webinar in the cycle Fundamentals of Machine Learning – Introduction to Machine Learning and the Programming Language "Python" will take place at Rīga Stradiņš University (RSU).
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 take on 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. The seminar is intended to provide insight into Machine Learning and 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 is currently one of the most popular programming languages.
Each webinar will be organised in two parts: theoretical and practical. In the practical part, participants will have the opportunity to implement machine learning algorithms using the Python programming language.
- An understanding of data structures, experience using electronic spreadsheets like Microsoft Excel for data analysis;
- Laptop or desktop PC with internet connection, microphone and webcam to enable active participation;
- Python IDE installed on computer (details will be provided after registration);
- Experience working in Python or any other programming language is not mandatory.
Upcoming webinars in this cycle
|30 October||Supervised learning - Linear regression|
|13 November||Supervised learning - Classification (Part I)|
|27 November||Supervised learning - Classification (Part I)|
|11 December||Unsupervised learning|