Upcoming SciNet Events

SciNet EventsRefresh calendars Add to google calendar
May,2019
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. Part of Neural Network Programming, 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. Part of Neural Network Programming, 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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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
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
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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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 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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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
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
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
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
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
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
1:30 pm
4:30 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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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
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
Thu 27th Jun
9:30 am
12:30 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
Thu 27th Jun
9:30 am
12:30 pm
Add event to google
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
Thu 27th Jun
9:30 am
12:30 pm
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Practical Introduction to machine learning for neuroimaging: classifiers, dimensionality reduction, cross-validation and neuropredict. How to apply machine learning to your data, even if you do not know how to program. Learn what is machine learning and get a high-level overview of few popular types of classification and dimensionality reduction methods. Learn (without any math) how support vector machines work. Learn how to plan a predictive analysis study on your own data? What are the key steps of the workflow? What are the best practices, and which cross-validation scheme to choose? How to evaluate and report classification accuracy? Learn which toolboxes to use when, with a practical categorization of few toolboxes. This is followed by detailed demo of neuropredict, for automatic estimation of predictive power of different features or classifiers without needing to code at all. Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Thu 27th Jun
1:30 pm
4:30 pm
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This course will teach about analyzing MRI data with R using traditional and bayesian methods. We will demonstrate general techniques using ROI level neuroanatomical analyses including structure volume and cortical thickness, and give you hands on practice with hierarchical modelling using the Stan probabilistic programming language. Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Thu 27th Jun
1:30 pm
4:30 pm
Add event to google
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
Thu 27th Jun
1:30 pm
4:30 pm
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A mix of lecture and hands-on to introduce specialized scientific visualization software such as ParaView and VisIt. Location: McLennan Physical Labs - MP103
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60 St. George Street, Toronto, M5S 1A7, Canada
Fri 28th Jun
9:30 am
12:30 pm
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Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Fri 28th Jun
9:30 am
12:30 pm
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This session gives an introduction to Julia, a programming language that was designed from the beginning for high performance. -- Prerequisite: some programming experience in another programming language. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 28th Jun
9:30 am
12:30 pm
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In this session, lectures and hands-on labs are interspersed, and the students will learn the basics of shared memory programming with OpenMP. In particular, we will discuss the OpenMP execution and memory model, performance, reductions and load balancing. -- Prerequisites: C, C++ or Fortran programming, experience editing and compiling code in a Linux environment. Part of Shared Memory Programming with OpenMP, Location: McLennan Physical Labs MP 137
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60 St. George Street, Toronto, M5S 1A7, Canada
Fri 28th Jun
1:30 pm
4:30 pm
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An afternoon of biomedical hacking. Bring your own code! Part of _HPC160 BYOC/Biomedical Hacking, Location: McLennan Physical Labs - MP134
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60 St George St., Toronto, M5S 1A7, Canada
Fri 28th Jun
1:30 pm
4:30 pm
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Debugging and profiling are important steps in developing a new code, or porting an old one to a new machine. In this session, we will discuss the debugging of frequently encountered bugs in serial code with gdb and the debugging of parallel (MPI and threaded) codes using ddt. If you bring your own code, and can explain your problem or concerns, we'll also give you advice and work with you to try to improve it. Part of Debugging and BYOC, Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada
Fri 28th Jun
4:30 pm
5:00 pm
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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. Part of Summer School Essentials, Location: McLennan Physical Labs - MP102
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60 St. George Street, Toronto, 60 St. George Street, Canada
July,2019
Tue 2nd Jul
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Location: Rotman School of Management - LL1025
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105 St. George Street, Toronto, M5S 3E6, Canada
Thu 4th Jul
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Location: Rotman School of Management - LL1025
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105 St. George Street, Toronto, M5S 3E6, Canada
Fri 5th Jul
9:00 am
5:00 pm
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A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. Location: Rotman School of Management - LL1025
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105 St. George Street, Toronto, M5S 3E6, Canada
August,2019
Mon 19th Aug
11:00 am
6:00 pm
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The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Tue 20th Aug
11:00 am
6:00 pm
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The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Wed 21st Aug
11:00 am
6:00 pm
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The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 22nd Aug
11:00 am
6:00 pm
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The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Fri 23rd Aug
11:00 am
6:00 pm
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The goal of this institute is to prepare attendees to be able to scale their computational codes to leadership-class computing systems via two-way video conferences with help of local analysts. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
September,2019
Mon 9th Sep
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Wed 11th Sep
10:00 am
11:30 am
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A quick introduction how to use SciNet and the Niagara supercomputer. Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 12th Sep
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 16th Sep
1:00 pm
2:00 pm
Add event to google
In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 16th Sep
1:00 pm
4:00 pm
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Working with many of the HPC systems in Ontario involves using the Linux 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 basic commands in the Linux environment. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 19th Sep
1:00 pm
2:00 pm
Add event to google
In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Mon 23rd Sep
1:00 pm
2:00 pm
Add event to google
In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada
Thu 26th Sep
1:00 pm
2:00 pm
Add event to google
In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 27th Sep
12:00 pm
1:00 pm
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Meetings for visualization enthusiasts to discuss and share ideas about visualization and novel data representations. Part of UofT Viz Discussion Group, Location: SciNet Boardroom MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 30th Sep
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
October,2019
Tue 1st Oct
1:00 pm
2:00 pm
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New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 3rd Oct
1:00 pm
2:00 pm
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New to programming? Learn the basics of programming using python in eight one-hour sessions over the course of four weeks. Sessions will consist of a mix of lectures and hands-on exercises. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Thu 3rd Oct
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
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1 King's College Circle, Toronto, M5S 1A8, Canada
Fri 4th Oct
1:00 pm
4:00 pm
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Introductory workshop to get started in the usage of version control GIT.This workshop is held in collaboration with UofT-Libraries. Location: SciNet Teaching Room MaRS 1140
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661 University Ave., Toronto, M5G 1M1, Canada
Mon 7th Oct
1:00 pm
2:00 pm
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In this course data analysis techniques utilizing the 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: 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 IMS graduate program and to be taught at the UofT St. George campus. Part of Introduction to Computational BioStatistics with R, Location: Medical Sciences Building, MS2170
Show in Google map
1 King's College Circle, Toronto, M5S 1A8, Canada