Computer Science, Second Cycle, Machine Learning

Machine learning is part of the field of artificial intelligence in computer science that uses techniques to teach robots or programs to perform a specific task intelligently based on data sets instead of being explicitly programmed. It can be about recognizing a face, determining the optimal time for machine maintenance, sorting text documents, having functioning speech recognition, recognizing handwritten characters, distinguishing patterns in large data sets, generating images and text, and much more. In this course, we will go over some of the most common algorithms for supervised and unsupervised learning, such as decision trees, k-means clustering, and artificial neural networks. This will give you the foundation to understand and discuss the latest machine learning techniques such as deep learning. You will also learn methods for analyzing and processing data, techniques for evaluating pre-trained models, and practical recommendations for applying machine learning algorithms for both classification and prediction to real-world problems. The course also includes a literature study that will give you an insight into the latest research in machine learning, as well as a practical classification problem that will give you practical experience in using machine learning. The course is intended for working professionals.