In recent years, deep neural networks have been used to solve complex machine-learning problems. They have achieved significant state-of-the-art results in many areas.
The goal of the course is to introduce deep neural networks, from the basics to the latest advances. The course will focus both on theory as well as on practical aspects (students will implement and train several deep neural networks capable of achieving state-of-the-art results, for example in named entity recognition, dependency parsing, machine translation, image labeling or in playing video games). No previous knowledge of artificial neural networks is required, but basic understanding of machine learning is advisable.
To pass the practicals, you need to obtain at least 80 points, which are awarded for home assignments. Note that up to 40 points above 80 will be transfered to the exam.
To pass the exam, you need to obtain at least 55, 70 and 85 out of 100 points for the written exam (plus up to 40 points from the practicals), to obtain grades 3, 2 and 1, respectively.