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July,2022
29 Jul 12:34 pm

Course Feedback opens

This course will provide an introduction to the theory, formalisms and algorithms of quantum computing.  The programming language will be Python; experience with Python will be assumed.  Experience with quantum mechanics is not necessary; an introductory-level understanding of linear algebra will be assumed.  The PennyLane quantum-computing programming framework (installation instructions here), provided by Xanadu, will be used.  No experience with quantum computing will be expected.  Classes will be held, July 25, 27, 29, 12:30-3:30pm, in the SciNet Teaching room. Update: due to technical problems with the SciNet Teaching room, this class will be moved to the SciNet Boardroom.
SCMP151 - Jul 2022
August,2022
1 Aug 12:00 am

Lecture 1, hands on 2 is due

This course will provide an introduction to the theory, formalisms and algorithms of quantum computing.  The programming language will be Python; experience with Python will be assumed.  Experience with quantum mechanics is not necessary; an introductory-level understanding of linear algebra will be assumed.  The PennyLane quantum-computing programming framework (installation instructions here), provided by Xanadu, will be used.  No experience with quantum computing will be expected.  Classes will be held, July 25, 27, 29, 12:30-3:30pm, in the SciNet Teaching room. Update: due to technical problems with the SciNet Teaching room, this class will be moved to the SciNet Boardroom.
SCMP151 - Jul 2022
5 Aug 12:00 am

Quantum Fourier Transform is due

This course will provide an introduction to the theory, formalisms and algorithms of quantum computing.  The programming language will be Python; experience with Python will be assumed.  Experience with quantum mechanics is not necessary; an introductory-level understanding of linear algebra will be assumed.  The PennyLane quantum-computing programming framework (installation instructions here), provided by Xanadu, will be used.  No experience with quantum computing will be expected.  Classes will be held, July 25, 27, 29, 12:30-3:30pm, in the SciNet Teaching room. Update: due to technical problems with the SciNet Teaching room, this class will be moved to the SciNet Boardroom.
SCMP151 - Jul 2022
5 Aug 12:00 pm

Course Feedback closes

This course will provide an introduction to the theory, formalisms and algorithms of quantum computing.  The programming language will be Python; experience with Python will be assumed.  Experience with quantum mechanics is not necessary; an introductory-level understanding of linear algebra will be assumed.  The PennyLane quantum-computing programming framework (installation instructions here), provided by Xanadu, will be used.  No experience with quantum computing will be expected.  Classes will be held, July 25, 27, 29, 12:30-3:30pm, in the SciNet Teaching room. Update: due to technical problems with the SciNet Teaching room, this class will be moved to the SciNet Boardroom.
SCMP151 - Jul 2022
September,2022
13 Sep 9:00 am 10:30 am

MSC1090: Lecture 1

The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language.  Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data.  Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.
Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in GB244 and BL205, respectively.
Students willing to take the course as part of their graduate program must enrol through Acorn.  This course is part of the IMS graduate program.
GB244
Show in Google mapMSC1090 - Fall 2022
14 Sep 10:00 am 11:30 am

Intro to SciNet, Niagara, and Mist

In about 90 minutes, learn how to use the SciNet systems Niagara and Mist, from securely logging in to running computations on the supercomputer. Experienced users may still pick up some valuable pointers. Format: Online
online
Show in Google mapHPC105 - Sep 2022
15 Sep 10:20 am 11:50 am

MSC1090: Lecture 2

The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language.  Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data.  Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.
Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in GB244 and BL205, respectively.
Students willing to take the course as part of their graduate program must enrol through Acorn.  This course is part of the IMS graduate program.
BL205
Show in Google mapMSC1090 - Fall 2022
20 Sep 9:00 am 10:30 am

MSC1090: Lecture 3

The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language.  Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data.  Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.
Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in GB244 and BL205, respectively.
Students willing to take the course as part of their graduate program must enrol through Acorn.  This course is part of the IMS graduate program.
GB244
Show in Google mapMSC1090 - Fall 2022
22 Sep 10:20 am 11:50 am

MSC1090: Lecture 4

The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language.  Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data.  Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.
Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in GB244 and BL205, respectively.
Students willing to take the course as part of their graduate program must enrol through Acorn.  This course is part of the IMS graduate program.
BL205
Show in Google mapMSC1090 - Fall 2022