Upcoming SciNet Events

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March,2019
Wed 27th Mar
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: HL 006 (UTSC)
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Highland Hall @ UTSC, Scarborough, , Canada
Thu 28th Mar
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 29th Mar
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: HL 008 (UTSC)
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Highland Hall @ UTSC, Scarborough, , Canada
April,2019
Tue 2nd Apr
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 3rd Apr
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: HL 006 (UTSC)
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Highland Hall @ UTSC, Scarborough, , Canada
Thu 4th Apr
11:00 am
12:00 pm
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This course is aimed at reducing your struggle in getting started with computational projects, and make you a more efficient computational scientist. Topics include well-established best practices for developing software as it applies to scientific computations, common numerical techniques and packages, and aspects of high performance computing. While we will introduce the C++ language, in one language or another, students should already have some programming experience. Despite the title, this course is suitable for many physical scientists (chemists, astronomers, ...). Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 5th Apr
11:00 am
12:00 pm
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In this course data analysis techniques utilizing Python and R statistical language, will be discussed and introduced, as well as, the basics of programming and scientific computing.The goal of this course is to prepare graduate students to perform scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data.Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.Students willing to take the course as part of their graduate program have to enroll through Acorn/ROSI.This course is part of the EES graduate program and to be taught at the UTSc campus. Location: HL 008 (UTSC)
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Highland Hall @ UTSC, Scarborough, , Canada
Wed 10th Apr
10:00 am
11:30 am
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A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 10th Apr
12:00 pm
1:00 pm
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Monthly user meeting at SciNet with pizza, a techtalk and user discussion:"Bayesian Model-Based Clustering Approaches for Discrete-Valued Gene Expression Data"by Anjali Silva.Abstract: Unsupervised classification or clustering uses no a priori knowledge of the labels of the observations in the process of categorizing data. This presentation focuses on research surrounding machine learning of discrete-valued gene expression datasets using clustering, with the aim of identifying gene co-expression networks. Specifically, a number of topics surrounding the use of mixture models and Markov chain Monte Carlo (MCMC) methods for clustering of discrete data from high-throughput transcriptome sequencing technologies will be presented. After outlining current challenges and gaps in research with respect to clustering approaches, several mixture model-based clustering methods will be presented. Significance, innovation, limitations and a number of future directions stemming from this research will be discussed. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 10th Apr
1:00 pm
4:00 pm
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Increase you Linux (bash) command line productivity. Requires some basic Linux command line experience. Part of Advanced Linux Command Line, Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 16th Apr
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 18th Apr
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 23rd Apr
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 25th Apr
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 30th Apr
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
May,2019
Wed 1st May
1:00 pm
4:00 pm
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Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 2nd May
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 3rd May
10:00 am
1:00 pm
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This workshop is part of the 2019 Chemical BioPhysics Symposium.This workshop will provide an introduction to some of the key methods and concepts in machine learning.We will present and discuss the following topics: - Introduction to machine learning. - Regression. - Bias-variance tradeoff. - Resampling Methods. - Classification algorithms, in general. - Decision trees, kNN, k-means. - Agglomerative clustering.We will use Python 3, and attendees are expected to be familiar with the Python programming language but extensive programming experience is not required - we will mainly be calling functions in existing packages, not writing large amounts of code. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 7th May
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th May
10:00 am
11:30 am
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A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th May
12:00 pm
1:00 pm
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Monthly user meeting at SciNet with pizza, a techtalk and user discussion:"Managing your data - key concepts", by Dylanne Dearborn and Leslie Barnes (UofT Libraries).We'll discuss some of the general principles of data management, why it's important, how it can improve your research process, and how it can increase your research impact. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 8th May
1:00 pm
4:00 pm
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Working with many of the HPC systems in Ontario involves using the Linux/UNIX command line. This provides a very powerful interface, but it can be quite daunting for the uninitiated. In this half-day session, you can become initiated with this course. This hands on session will cover basic commands and scripting. It could be a great boon for your productivity Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 9th May
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 16th May
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 21st May
2:30 pm
4:00 pm
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This workshop is part of the 2019 Toronto BrainHack.A quick introduction how to use SciNet and the Niagara supercomputer in the context of the CAN-ACN BrainHack 2019. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 23rd May
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 30th May
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
June,2019
Tue 4th Jun
9:00 am
11:00 am
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A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 6th Jun
11:00 am
12:00 pm
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This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.7; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 12th Jun
10:00 am
11:30 am
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A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 12th Jun
12:00 pm
1:00 pm
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Monthly user meeting at SciNet with pizza, a techtalk and user discussion on "No Conda: Using Python, Installing Packages, and Accessing Jupyter Notebooks on Niagara". Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 24th Jun
9:00 am
to Fri 28th Jun
5:00 pm
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The Compute Ontario Summer School on Scientific and High Performance Computing is an annual educational event for graduate/undergraduate students, postdocs and researchers who are engaged in a compute intensive research. Held geographically in the west, centre and east of the province of Ontario, the summer school provides attendees with the opportunity to learn and share knowledge and experience in high performance and technical computing on modern HPC platforms. Part of CO Summer School Central, Location: University of Toronto - St. George Campus
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100 St George St, Toronto, M5S 3G3, Canada
Mon 24th Jun
9:30 am
10:30 am
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This session will show how to use te resources for the summer school, will test the guest accounts, and will explain the attendance taking. Mandatory for all participants. Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada
Mon 24th Jun
10:45 am
12:30 pm
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An introduction to basic concepts of high-performance computing. It is intended to be a high level primer for those largely new to HPC, and serve as a foundation upon which to build over the coming days. Topics will include motivation for HPC, available HPC resources, essential issues, problem characteristics as they apply to parallelism, and a high level overview of parallel programming models. -- Prerequisites: C, C++ or Fortran programming, experience editing and compiling code in a Linux environment. Location: McLennan Physical Labs MP 137
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60 St. George Street, Toronto, M5S 1A7, Canada
Mon 24th Jun
10:45 am
12:30 pm
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Working with HPC systems involves using the Linux command line. This short session will cover basic linux commands to get you initiated with the command line. Part of Introduction to Linux Command Line, Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada
Mon 24th Jun
10:45 am
12:30 pm
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Master the Linux command line and its processes; explore filesystems to check directory status and access modification times; create and manage user and group passwords securely. -- Prerequisite: some understanding of Linux CLI, be able to open a terminal, and use simple commands Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Mon 24th Jun
1:30 pm
4:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. -- Prerequisites: C/C++ or Fortran programming. Part of Programming Clusters with Message Passing Interface, Location: McLennan Physical Labs MP 137
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60 St. George Street, Toronto, M5S 1A7, Canada
Mon 24th Jun
1:30 pm
4:30 pm
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This half-day session offers a brief introduction to R, with a focus on data analysis and statistics. We will discuss and introduce the following topics: the R interface, primitive data types, lists, vectors, matrices, and data frames - a crucial data type in data analysis and a trademark in the R language. Advanced topics to be covered include: basics statistics and function creation; *apply family functions; and basics of scripting. Time depending we may cover and discuss some data management strategies (ie. saving results, workspaces and installing packages) and basic plotting. -- Prerequisite: some programming experience in another programming language. Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada
Mon 24th Jun
1:30 pm
4:30 pm
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This 3 hours course will focus of data cleaning and statistical analysis of large scale genetic data using PLINK and R softwares. We will use SciNet to run the PLINK analytical workflow to perform markers and individuals quality control, including population stratification and ancestry followed by association analysis between genetic variation and trait of interest. R-software (basic R plotting and ggplot package) will be use to visualize the results. -- Prerequisite: basic Linux command line skills and R Location: McLennan Physical Labs - MP103
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60 St. George Street, Toronto, M5S 1A7, Canada
Tue 25th Jun
9:30 am
12:30 pm
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In this half-day session, you will be taught how to use python for research computing purposes. Topics include: the basics of python, automation, numpy, scipy, file i/o, and visualization. -- Prerequisite: some programming experience in another programming language. Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada
Tue 25th Jun
9:30 am
12:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. -- Prerequisites: C/C++ or Fortran programming. Part of Programming Clusters with Message Passing Interface, Location: McLennan Physical Labs MP 137
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60 St. George Street, Toronto, M5S 1A7, Canada
Tue 25th Jun
9:30 am
12:30 pm
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Next generation sequencing is revolutionizing the way molecular sciences and biology is performed while providing a population-level understanding of genetic variation in organisms. In this course, you will be introduced to data types in next generation sequencing, analysis methods and best practices to go from raw sequencing data to fully reconstructed genomes. Attendees will also be exposed to variant calling methods to assess genetic variation and the use of parallel methods to scale large analysis on a high-performance computing cluster. Principles in this course can be applied to the other workshops in this stream for genome-wide association analysis and RNA sequencing analysis. -- Prerequisite: basic Linux command line skills Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Tue 25th Jun
1:30 pm
4:30 pm
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In this 1.5-day session, through lectures interspersed with hands-on labs, the students will learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. -- Prerequisites: C/C++ or Fortran programming. Part of Programming Clusters with Message Passing Interface, Location: McLennan Physical Labs MP 137
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60 St. George Street, Toronto, M5S 1A7, Canada
Tue 25th Jun
1:30 pm
4:30 pm
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This half-day session will cover parallel programming in python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. -- Prerequisites: python programming and experience editing code in a Linux environment. Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada
Tue 25th Jun
1:30 pm
4:30 pm
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The class will cover basic pipeline of pre-alignment QC of FASATQ files, read alignments to the reference genome, Post alignment visualization using IGV, and differential expression analysis using R. If time permits, enrichment analysis using GSEA will also be covered. -- Prerequisites: basic Linux command line skills and R Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Wed 26th Jun
9:30 am
12:30 pm
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This half-day session offers an overview of machine learning tools available in Python. -- Prerequisites: python programming Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 26th Jun
9:30 am
12:30 pm
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The course will introduce participants to the neuroimaging data and best practices for the analysis of neuroimaging data using High Performance Clusters (HPC). We will introduce types of neuroimaging scanning modalities with instructions for how to organize these data using the Brain Imaging Data Structure (BIDS). We will then introduce Singularity container software (BIDS-apps) for the preprocessing of neuroimaging data (including mriqc and fmriprep) and demonstrate how to run them on the HPC. We will discuss general information about running Singularity containerized software on the HPC and how to construct custom containers for your own analysis using NeuroDocker. -- Prerequisites: basic Linux command line skills Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Wed 26th Jun
9:30 am
12:30 pm
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This is an introductory course covering programming and computing on GPUs (graphics processing unit) which are an increasingly common presence in massively parallel computing architectures. The basics of GPU programming will be covered, and students will work through a number of hands on examples. The structuring of data and computations that makes full use of the GPU will be discussed in detail. This year the course will expand to cover the new features available on the GPUs installed on the Graham supercomputer at the University of Waterloo. Students should be able to leave the course with the knowledge necessary to begin developing their own GPU applications. -- Prerequisites: C/C++ scientific programming, experience editing and compiling code in a Linux environment. Some experience with CUDA and/or OpenMP a plus. Location: McLennan Physical Labs MP 137
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60 St. George Street, Toronto, M5S 1A7, Canada
Wed 26th Jun
1:30 pm
4:30 pm
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Python is increasingly becoming common-place in the analysis of neuroimaging data. This course will familiarize attendees with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). We will begin with first developing an understanding how MRI data is represented in Python and perform some hands-on tasks such as basic manipulation on both structural MR and functional MR. Then we will discuss the steps required to take minimally pre-processed MR data (fmriprep), to clean and workable data through the process of motion cleaning and dimensionality reduction. The final component of the course will involve performing functional connectivity (FC) analysis to build a resting state connectivity matrix. All analyses will be performed using Jupyter notebooks in the spirit of reproducible and open science. Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Wed 26th Jun
1:30 pm
4:30 pm
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These two half-day sessions will introduce neural network programming concepts, theory and techniques in Python. -- Prerequisites: python programming Part of Introduction to Neural Networks with Python, Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada