Teaching

Machine Learning

Undergraduate course, 2019

Machine learning is the subfield of computer science that, according to Arthur Samuel, gives “computers the ability to learn without being explicitly programmed.” Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term “machine learning” in 1959 while at IBM. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data– such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.

Deep Learning

Master's degree/ Doctor's degree course, 2019

Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where they produced results comparable to and in some cases superior to human experts. Through this course, we will learn deep learning from basic concepts to high-level techniques, and apply deep learning techniques to resolve practical problems.