Master’s KTH Royal Institute of programme in Machine Science and
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Master’s programme in Machine Learning Machine Learning is an area within Computer Science where computer systems are designed to learn from large sets of examples, similarly to the learning strategies of biological systems (like humans). Recently, Machine Learning has gained great importance for the design of search engines, robots and sensor systems, and for the processing of large scientific data sets. The focus of the Master’s program in Machine Learning is on mathematical foundations and methods for Machine Learning. The student can choose to study applications of this in Perception and Cognition or in Information Retrieval. Thanks to the heavy focus on mathematical foundations, graduates from the Machine Learning programme have a good background for doctoral studies in Machine Learning, Artificial Intelligence, Computer Vision, Speech Technology, Language Technology, and Information Retrieval. An graduate from the Machine Learning programme will also be well equipped to work in the sensor technology industry, with large companies such as Trimble, FLIR, Microsoft, IBM, and many smaller companies in the healthcare, car safety and visual surveillance areas. Graduates profiled towards Information Retrieval can be expected to be attractive to companies that apply Machine Learning technology to Information Retrieval tasks, such as Google, Yahoo, and a large number of smaller companies in the database, data mining and information retrieval areas. The programme Machine Learning is a two-year (120 ECTS credits) master’s programme. The instruction language is entirely English. The program consists of a basic curriculum of Machine Learning, AI, Computer Vision and Theory of Science (27 ECTS credits), followed by one of two tracks: (i) Perception and Cognition, and (ii) Information Retrieval. Track I: Perception and Cognition An important aspect of an intelligent system is the ability to observe and understand its environment. All intelligent biological systems have this ability; we humans perceive the world trough vision, hearing, taste, smell and touch. Sensing for artificial systems is developed within the fields of Speech Understanding and Image Understanding, also known as Computer Vision. Machine learning has come to dominate algorithmic construction in both these fields. The reason is the complexity and uncertainty of the visual and acoustic signals. In the Perception and Cognition track, the student will be acquainted with different Machine Learning methods for artificial perception, in particular Computer Vision, which is a large and active field of research at KTH. KTH Royal Institute of Technology is one of the top European universities within Science and Technology. A total of 17 000 students and a staff of 3 500 make it the largest technical university in Sweden. For 180 years, KTH has earned a reputation for pioneering research, engi- neering genius and science on the cutting edge. KTH is internationally minded with a large proportion of students from all over the world. Degree awarded Master of science Duration 120 ECTS, 2 years Contact Hedvig Kjellström, [email protected] Campus KTH Campus (Valhallavägen) Application deadline for programme start autumn 2011 January 15, 2011
Transcript of Master’s KTH Royal Institute of programme in Machine Science and
Master’s programme in Machine Learning Machine Learning is an area within Computer Science where computer systems are designed to learn from large sets of examples, similarly to the learning strategies of biological systems (like humans). Recently, Machine Learning has gained great importance for the design of search engines, robots and sensor systems, and for the processing of large scientific data sets. The focus of the Master’s program in Machine Learning is on mathematical foundations and methods for Machine Learning. The student can choose to study applications of this in Perception and Cognition or in Information Retrieval. Thanks to the heavy focus on mathematical foundations, graduates from the Machine Learning programme have a good background for doctoral studies in Machine Learning, Artificial Intelligence, Computer Vision, Speech Technology, Language Technology, and Information Retrieval. An graduate from the Machine Learning programme will also be well equipped to work in the sensor technology industry, with large companies such as Trimble, FLIR, Microsoft, IBM, and many smaller companies in the healthcare, car safety and visual surveillance areas. Graduates profiled towards Information Retrieval can be expected to be attractive to companies that apply Machine Learning technology to Information Retrieval tasks, such as Google, Yahoo, and a large number of smaller companies in the database, data mining and information retrieval areas.
The programme Machine Learning is a two-year (120 ECTS credits) master’s programme. The instruction language is entirely English. The program consists of a basic curriculum of Machine Learning, AI, Computer Vision and Theory of Science (27 ECTS credits), followed by one of two tracks: (i) Perception and Cognition, and (ii) Information Retrieval.
Track I: Perception and Cognition An important aspect of an intelligent system is the ability to observe and understand its environment. All intelligent biological systems have this ability; we humans perceive the world trough vision, hearing, taste, smell and touch. Sensing for artificial systems is developed within the fields of Speech Understanding and Image Understanding, also known as Computer Vision. Machine learning has come to dominate algorithmic construction in both these fields. The reason is the complexity and uncertainty of the visual and acoustic signals. In the Perception and Cognition track, the student will be acquainted with different Machine Learning methods for artificial perception, in particular Computer Vision, which is a large and active field of research at KTH.
KTH Royal Institute of Technology is one of the top European universities within Science and Technology. A total of 17 000 students and a staff of 3 500 make it the largest technical university in Sweden. For 180 years, KTH has earned a reputation for pioneering research, engi-neering genius and science on the cutting edge. KTH is internationally minded with a large proportion of students from all over the world.
Degree awarded Master of science Duration 120 ECTS, 2 years Contact Hedvig Kjellström, [email protected] Campus KTH Campus (Valhallavägen) Application deadline for programme start autumn 2011 January 15, 2011
Another focus of the Perception and Cognition track is the relation between Machine Learning and Neuroscience, where the functionality of neural systems such as the human brain is studied. Examensarbete
Track II: Information Retrieval During the last decade, there has been an explosive growth in the amount of data available, both on the Internet and in applications such as Economics, Medicine, Meteorology, Geoscience, and Astronomy. Machine Learning has come to be an effective tool for developing methods to search and organize large volumes of data. This process is often referred to as Data Mining, Knowledge Discovery or Information Retrieval. A number of successful companies, such as Google, build their entire product on Machine Learning methods for Information Retrieval. In the Information Retrieval track, the student will be acquainted with the mathematical foundations of Machine Learning for Information Retrieval, but also with technology for database construction and data security. There will be a focus on Information Retrieval in very large datasets, and on Information Retrieval of data with uncertainty, such as free text or images; these are the two problem domains in which Machine Learning aspects become most important.
Degree project The last semester is dedicated to a degree project, performed either at a research lab at KTH, in industry, or in a research lab at another university.
Prerequisites General admission requirements: See the KTH general admission requirements for Master’s programs. Specific admission requirements: The prerequisites for the Master's program in Machine Learning is a level in Mathema-tics and Computer Science equal or higher than that of the following courses at KTH: SF1604 Linear Algebra (or SF1624), SF1600 Calculus in One Variable (or SF1602, SF1625), SF1601 Calculus in Several Variables (or SF1603, SF1626), SF1906 Mathematical Statistics, DD1340 Introduction to Computer Science (or DD1320, DD1321, DD1344).
Application Application for program start the coming fall is done at www.studera.nu, January 15 at the latest.
More information: http://www.kth.se/studies/programmes/master/programmes/it/machinelearning?l=en_UK
