丁香园AV

Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.

The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.

Concepts and problem-solving techniques that are used in the design and analysis of efficient algorithms. Special consideration and adaptations for big data applications will be emphasized. Students with credit for CMPT 705 may not take this course for further credit.

The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.

From health care to social media the world generates a tremendous amount of data every day, often too much to be processed on a single computer or even some-times a single data centre. In this graduate seminar we will learn about technologies and systems behind Big Data. In particular, we will discuss what challenges exist in processing and storing massive amounts of data. We will explore how these challenges are being solved in real-world systems as well as the limitations inherent in these designs. The evolution of these technologies will be explored by reading both current and historically significant research papers. Prerequisite: Operating Systems (CMPT 300) and Data Base Systems (CMPT 354), or equivalents. Students with credit for CMPT 886 when offered as a Special Topics course in Big Data may not take this course for further credit.

Advanced topics in the field of scientific and information visualization are presented. Topics may include: an introduction to visualization (importance, basic approaches and existing tools), abstract visualization concepts, human perception, visualization methodology, 2D and 3D display and interaction and their use in medical, scientific, and business applications. Prerequisite: CMPT 316, 461 or equivalent (by permission of instructor). Students with credit for CMPT 878 or 775 may not take this course for further credit.

In this course, theoretical and applied issues related to the development of natural language processing systems and specific applications are examined. Investigations into parsing issues, different computational linguistic formalisms, natural language syntax, semantics, and discourse related phenomena will be considered and an actual natural language processor will be developed.

Provides a cognitive and computational framework for understanding and designing graphical and visual representations. Investigates several psychological and computational models of diagram processing, and explores diverse interactive graphical systems.

An advanced treatment of modern methods of multivariate statistics and non-parametric regression. Topics may include: (1) dimension reduction techniques such as principal component analysis, multidimensional scaling and related extensions; (2) classification and clustering methods; (3) modern regression techniques such as generalized additive models, Gaussian process regression and splines. Prerequisite: STAT 830 and STAT 853 or permission of instructor.

A study of the social theory of information technologies, examining issues affecting computer-mediated communication.

Examination of recent literature and problems in bioinformatics. Within the CIHR graduate bioinformatics training program, this course will be offered alternatively as the problem-based learning course and the advanced graduate seminar in bioinformatics (both concurrent with MBB 829). Prerequisite: Permission of the instructor.

This course aims to give students experience to emerging important areas of computing science. Prerequisite: Instructor discretion.

Examines current research topics in computer graphics, human computer interaction (including audio), computer vision and visualization.

This course is one of two lab courses that are part of the Professional Master鈥檚 Program in Big Data in the School of Computing Science. This lab course aims to provide students with the hands-on experience needed for a successful career in Big Data in the information technology industry. Many of the assignments will be completed on massive publically available data sets giving them appropriate experience with cloud computing and the algorithms and software tools needed to master programming for Big Data. Over 13 weeks of lab work and 12 hours per week of lab time, the students will obtain a solid background in programming for Big Data.

This course is one of two lab courses that are part of the Professional Masters Program in Big Data in the School of Computing Science. This lab course aims to provide students with the hands-on experience needed for a successful career in Big Data in the information technology industry. Many of the assignments will be completed on massive publically available data sets giving them appropriate experience with cloud computing and the algorithms and software tools needed to master programming for Big Data. Over 13 weeks of lab work and 12 hours per week of lab time, and building on the previous lab course CMPT 731, the students will obtain a solid background in programming for Big Data. Prerequisite: CMPT 732: Programming for Big Data 1.