Machine learning is an essential topic for many research areas, and across multiple departments at LTH, including applied mathematics, automatic control, computer science, medical imaging, signal processing, and mathematical statistics. It brings forward novel methodologies with transformative potential in big data analysis, specifically in application areas like image understanding (computer vision, image analysis), indexing and web search engines, natural language processing, robotics, or data analytics. The past years have witnessed an explosion of large-scale fully trainable methodologies based on hierarchical processing within the emerging fields of deep learning and reinforcement learning. This development is now in full swing, generating new research results in areas as diverse as automatic translation or game playing, and is beginning to successfully address some of the most profound open problems of intelligent processing.
Assessment: Compulsory assignments including computer work and written reports. Approved results on these are enough to pass the course. For those who wish a higher grade there will be a written exam on January 9th, from 14.00 to 18.00.
NB: If you are registered to the course in LADOK, but your Moodle account does not have access to the course, then please email the course email. Include the name and email address that you have listed in your Moodle account.
coach etc: Cristian Sminchisescu