Welcome to the official blog for the Machine Learning course at Wroclaw University of Science and Technology.
This course provides a comprehensive introduction to machine learning, covering both theoretical foundations and practical applications. Students will explore fundamental algorithms, modern techniques, and real-world implementations of machine learning systems.
The course is hosted by the Faculty of Pure and Applied Mathematics (WMAT) at WUST and offers hands-on experience with industry-standard tools and frameworks.
In today's lecture, we'll explore essential techniques for efficiently training and evaluating neural networks, with a particular focus on understanding and interpreting learning curves. We'll examine how to diagnose common training issues, optimize the learning process, and develop intuition for model behavior through careful analysis of loss and accuracy metrics throughout the training cycle.
In today's lecture, we'll dive into multilayer perceptrons (MLPs), understanding how these more sophisticated neural networks overcome the limitations of single-layer models by utilizing hidden layers, backpropagation for learning, and their ability to solve complex non-linear problems that form the foundation of modern deep learning architectures.
The review of the course syllabus, grading policy, assignment schedule, and available resources to ensure everyone understands the expectations and logistics for our journey into machine learning and reproducible research.
In today's lecture, we'll explore the foundational journey of neural networks, starting with the McCulloch-Pitts neuron model and progressing through Rosenblatt's perceptron, examining their capabilities with logic gates, understanding the XOR problem limitation, and discovering how these early challenges shaped the development of more advanced network architectures.
The review of the liner regression models in the context of machine learning and the usage of gradient descent method.
In the second part of our first lecture, we will embark on an exciting journey into the world of machine learning (ML) and the critical importance of reproducible research.