{"ModuleCode":"EE5934","ModuleTitle":"Deep Learning","AcadYear":"2018/2019","Department":"Electrical & Computer Engineering","ModuleDescription":"Deep learning refers to machine learning methods based\non deep neural networks to learn data representation and\nperform learning tasks. This course provides an\nintroduction to deep learning. Students taking this course\nwill learn the basic theories, models, algorithms, and\nrecent progress of deep learning, and obtain empirical\nexperience. The course starts with machine learning\nbasics and classical neural network models, followed by\ndeep convolutional neural networks, recurrent neural\nnetworks, reinforcement learning and applications to\ncomputer vision and speech recognition. The students are\nexpected to have good knowledge of calculus, linear\nalgebra, probability and statistics as a prerequisite.","ModuleCredit":"4","Workload":"3-0-0-4-3","Prerequisite":"- University level calculus, linear algebra\n- Probability and statistics (e.g. EE2012)\n- Scientific programming language, such as Python (e.g. IT1007).\nAll class assignments will be in Python (and use NumPy).","Preclusion":"EE6934 Deep Learning (Advanced)","History":[{"Semester":2,"Timetable":[{"LessonType":"Lecture","ClassNo":"01","DayText":"Monday","StartTime":"1800","EndTime":"2100","WeekText":"Every Week","Venue":"LT4"}],"LecturePeriods":["Monday Evening"]}],"ParsedPrerequisite":{"or":["EE2012","IT1007"]},"ParsedPreclusion":"EE6934","ModmavenTree":{"name":"EE5934","children":[{"name":"or","children":[{"name":"EE2012","children":[]},{"name":"IT1007","children":[]}]}]},"LockedModules":[]}