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Unformatted text preview: 11s1: COMP9417 Machine Learning and Data Mining Course Outline March 1, 2011 Learning objectives and outcomes As a result of successfully completing this course students will have a working knowledge of key topics in machine learning, and will be able to demonstrate their knowledge both by describing aspects of the topics and by solving problems related to the topics. They will have practical experience with the topics covered in the assignments undertaken. By the end of the subject, students should be able to: • set up a well-defined learning problem for a given task • select and define a representation for data to be used as input to a machine learning algorithm • select and define a representation for the model to be output by a machine learning algorithm • compare different algorithms according to the properties of their inputs and outputs COMP9417: March 1, 2011 Course Intro: Slide 1 • compare different algorithms in terms of similarities and differences in the computational methods used • develop and describe algorithms to solve a learning problem in terms of the inputs, outputs and computational methods used • express key concepts from the foundations of computational and statistical learning theory and demonstrate their applicability • express knowledge of general capabilities and limitations of machine learning from computational and statistical theory • use or extend or invent algorithms in applications to real-world data sets and collect results to enable evaluation and comparison of their performance COMP9417: March 1, 2011 Course Intro: Slide 2 Staff Staff Name Role Email Oﬃce Extension Mike Bain Lecturer & [email protected] K17-401H 9385 6935 Coordinator Contact lecturer to arrange consultation. COMP9417: March 1, 2011 Course Intro: Slide 3 Course Web Pages http://www.cse.unsw.edu.au/~cs9417/ COMP9417: March 1, 2011 Course Intro: Slide 4 Course Information • Units of credit – 6 • Parallel teaching – no, only COMP9417 students attend the class – The students include postgraduates and senior undergraduates COMP9417: March 1, 2011 Course Intro: Slide 5 Assumed knowledge/prerequisites Prerequisites are COMP9024 Data Structures and Algorithms or COMP1921 Data Structures and Algorithms (check with University handbook). Waivers can be granted where applicable (arrange to see Course Coordinator). Mathematical assumed knowledge is completion of basic university mathematics courses, such as the UNSW courses MATH1131 and MATH1231. Additionally, in practice, some knowledge of basic probability, statistics and logic will be the starting point for some of the course materials. Ability to program and construct working software in a general-purpose programming language (e.g., Java) is assumed....
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This note was uploaded on 06/20/2011 for the course COMP 9417 taught by Professor Some during the Three '11 term at University of New South Wales.
- Three '11
- Machine Learning