|
|
This seven-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. 14 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-06 2019-04 2018-04 |
|
|
In lieu of its annual Ontario Summer School, SciNet in collaboration with CAMH will be offering weekly virtual summer training on High Performance Computing from June through to August. Topics will include parallel programming, Linux shell, large scale batch processing, biomedical computations, and performance Python and R. The schedule will be announced on the site in the near future. 0 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-06 |
|
|
|
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. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-06 2019-06 2018-06 2015-07 2014-06 2013-05 |
|
|
|
|
|
|
|
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. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-06 2019-10 2019-06 2018-06 2015-07 2014-06 2013-05 |
|
|
|
Learn the basics of Message Passing Interface (MPI) programming. Examples and exercises will be based on parallelization of common scientific computing problems. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-07 2019-10 2019-06 2018-06 |
|
|
|
Introduction 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. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-07 2019-06 2018-06 |
|
|
|
Get familiar with how one can manipulate and transform neuroimaging data using Python s neuroimaging packages (nibabel, nilearn). Develop 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. Finally, we will cover how to perform 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. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-07 2019-06 2018-06 |
|
|
|
Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-07 2019-06 2018-06 2015-07 |
|
|
|
Learn how to model the neural network in the brain using Python. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-08 2019-06 |
|
|
|
Learn parallel programming R, with a focus on parallel data analysis. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-08 2015-07 |
|
|
|
Learn how to perform data cleaning and statistical analysis of large scale genetic data using PLINK and R software. 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. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-08 2019-06 2018-06 |
|
|
|
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. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-08 2019-06 2018-06 2015-07 |
|
|
This course is an introductory course in programming utilizing the R Statistical Language.
The course is restricted to student of the UofT's Biochemistry departments. Students interested should register though their graduate coordinator. 12 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-04 |
|
|
|
|
|
Learn how to write bash scripts, use environment variables, how to control process, and much more. Requires some linux basic command line experience.
180 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-02 2019-02 2018-10 2017-10 |
|
|
|
|
|
Apply MPI to realistic scientific computing examples and learn to use advanced MPI techniques such as non-blocking communications. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-02 |
|
|
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.
24 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-01 2019-01 2018-01 2017-01 |
|
|
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, ...). 25 events of 57.6 minutesClick on one of the following instances for course materials such as slides and recordings:
2020-01 2019-01 2018-01 2017-01 2016-01 |
|
|
|
The goal is for students, new to GPGPU but familiar with programming in C/C++, to leave being able to write simple kernels for their own problems, and understand the tools and techniques needed to improve the results. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-11 2019-06 2018-06 2016-05 2015-04 |
|
|
Learn about research computing even with little programming experience. Covers programming in python, best practices and visualization. Some experience with python is required. The course will last 4 weeks with 2 lectures per week (mini/modular grad course). 8 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-11 2018-11 2017-11 2016-11 2015-11 2014-11 |
|
|
|
Increase you Linux (bash) command line productivity. Requires some basic Linux command line experience.
180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-10 2019-06 2019-04 |
|
|
Short workshop aimed to discuss the Basics of Python, basic plotting capabilities, curve fitting, pandas and data frames. 120 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-10 |
|
|
Introductory workshop to get started in the usage of version control GIT.
This workshop is held in collaboration with UofT-Libraries. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-10 |
|
|
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. 8 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-10 2018-10 2017-10 |
|
|
Meetings for visualization enthusiasts to discuss and share ideas about visualization and novel data representations. 3 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-09 |
|
|
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.
25 events of 57.6 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-09 2018-09 2018-01 2017-09 |
|
|
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.
5 events of 420 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-08 2017-06 |
|
|
A machine learning bootcamp given to participants of the Quantum Machine Learning program by Creative Destruction Labs. 3 events of 480 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-07 2018-06 |
|
|
|
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. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 |
|
|
|
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. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 |
|
|
|
A mix of lecture and hands-on to introduce specialized scientific visualization software such as ParaView and VisIt. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 |
|
|
|
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. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 |
|
|
|
These two half-day sessions will introduce neural network programming concepts, theory and techniques in Python. -- Prerequisites: python programming 2 events of 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 2018-06 2017-09 2017-05 |
|
|
|
This half-day session offers an overview of machine learning tools available in Python. -- Prerequisites: python programming 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 2019-05 2018-06 |
|
|
|
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 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 2018-06 |
|
|
|
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 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 |
|
|
|
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. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 2018-06 2018-06 2015-07 |
|
|
|
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. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 2018-06 2017-05 2015-07 |
|
|
|
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. 6240 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-06 2018-06 2017-07 2016-07 2015-07 2014-06 2013-05 |
|
|
Principles and uses of relational databases with practical examples using python and sqlite on the Niagara supercomputer. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-05 2018-06 2017-06 2016-05 2015-05 |
|
|
The Institute for Data Intensive Engineering and Sciences will jointly host a Visualization Hackathon with the University of Toronto. The Hackathon will be on Friday & Saturday, January 25 & 26, 2019 from 9 AM – 5 PM (approx), with sessions and presentations at both Homewood Campus at JHU, and SciNet headquarters at the University of Toronto. We will focus on topics and techniques relevant to data-intensive and computationally-intensive research. This event will feature some presentations on techniques and tools, but will be primarily hands on and participant driven.
http://idies.jhu.edu/idies-to-co-host-visualization-hackathon-with-university-of-toronto/
http://idies.jhu.edu/news-events/visualization-hackathon/
Registration:
http://idies.jhu.edu/news-events/visualization-hackathon/#registration-form 2 events of 480 minutesClick on one of the following instances for course materials such as slides and recordings:
2019-01 |
|
|
|
An afternoon of biomedical hacking. Bring your own code! 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2018-06 |
|
|
|
How to find and use public datasets for neuroscience research with a focus on transcriptomics and neuroimaging. Introduction and guides to some of the largest datasets will be provided (Allen human brain atlases, BrainEAC, ADNI, and the Human Connectome Project). 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2018-06 |
|
|
|
A mix of lecture and hands-on to introduce specialized scientific visualization software such as ParaView and VisIt. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2018-06 |
|
|
|
This course will teach about analyzing MRI data with R. We will focus on volumes/cortical thickness, etc., and teach both classic massively univariate as well as (to us!) more interesting hierarchical bayesian approaches. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2018-06 2016-06 |
|
|
|
This 1/2 day course will focus of cortical surface based neuroimaging analysis. We will talk use SciNet to run freesurfer's recon-all pipeline to define the cortical surfaces in our datasets. We will then use Connectome-Workbench (tools from the Human Connectome Project, or HCP) to analyse and visualize our data. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2018-06 |
|
|
|
Introductory seminar series on Scientific Computing, High-Performance Computing, Data Science and Visualization; this lecture series is part of the "Research Project Course" (PHY479Y1) for 4th year undergraduate physics students. 7 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2017-10 2017-01 |
|
|
|
This class will review advanced neural network programming theory and architectures. The level of the material will not be introductory, experience with neural networks will be assumed. This class is intended to continue the material covered in DAT111 (Introduction to Neural Network Programming).
4 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2017-10 |
|
|
|
Explore and use advanced examples of parallel computing in scientific research (mini/modular graduate course). 9 events of 66.666666666667 minutesClick on one of the following instances for course materials such as slides and recordings:
2017-09 2016-09 |
|
|
|
A Python Bootcamp is given to partipantss of the Quantum Machine Learning program by Creative Destruction Labs 3 events of 420 minutesClick on one of the following instances for course materials such as slides and recordings:
2017-08 |
|
|
Learn how to pinpoint and alleviate bottlenecks in large data-driven research projects. Techniques such as tar, compression, ramdisk, and file format options will be covered. 420 minutesClick on one of the following instances for course materials such as slides and recordings:
2017-05 2016-09 2016-04 |
|
|
The goal of this course is to prepare 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.
This course can be taken as "Seminars in Translational Research" (MSC1010Y-1011Y) for students in the "Institute of Medical Science".
This course can also be taken by PhD students for graduate credits from the Department of Ecology & Evolutionary Biology (EEB) at the UofT. Interested students from the EEB department should contact Prof. Helen Rodd in advance.
The course can also be taken as a mini/modular graduate course by Physics students. 12 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2016-10 2015-10 |
|
|
Information sessions for the 2017 Big Data Challenge for High Schools Students. 3 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2016-10 2015-10 2015-10 2014-11 2014-10 |
|
|
|
Live broadcast of the Q&A session for the 2016 Visualization Challenge: "Visualize this!/Faites-nous voir ça!" competition 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2016-09 |
|
|
|
Visualization session at the Ontario HPC Summer School East, at the University of Ottawa hosted by CAC. 420 minutesClick on one of the following instances for course materials such as slides and recordings:
2016-08 2016-05 2015-07 |
|
|
|
Trends and tools in research data management. 210 minutesClick on one of the following instances for course materials such as slides and recordings:
2016-05 |
|
|
|
Best practices course given at GLBIO 2016. 390 minutesClick on one of the following instances for course materials such as slides and recordings:
2016-05 2014-06 2013-05 |
|
|
|
Virtual seminar by Allinea (makers of the DDT debugger) on their parallel profiling and performance tools. 120 minutesClick on one of the following instances for course materials such as slides and recordings:
2016-03 |
|
|
|
This class will introduce students to using Apache Spark on the GPC. The Python interface PySpark will used to access the Spark infrastructure. Students are encouraged to bring a laptop to the class, so as to follow along with the exercises and quizzes. Students will be introduced to the PySpark syntax and commands, techniques for loading and managing data, and various data analysis strategies. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2015-12 |
|
|
As part of the 2008 Fortran standard, Coarray Fortran is a minimal extension to the Fortran language that allows distributed parallel computing, by giving access to arrays held by other processes, without explicit message passing. 120 minutesClick on one of the following instances for course materials such as slides and recordings:
2015-11 |
|
|
Learn how to avoid I/O becoming the bottleneck in your large-scale computation; general strategies as well as true parallel I/O techniques (MPI-IO, HDF5, NetCDF, ..) will be covered in this half-day course. 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2015-09 2013-02 |
|
|
|
A HPCS2015 tutorial on how to plan and design storage management and I/O patterns in order to prevent bottlenecks in data-driven projects. 210 minutesClick on one of the following instances for course materials such as slides and recordings:
2015-06 |
|
|
|
Third of three mini/modular courses on Scientific Computing. 8 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2015-03 2013-03 |
|
|
|
Second of three mini/modular courses on Scientific Computing. 8 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2015-02 2013-02 |
|
|
|
First of three mini/modular courses on Scientific Computing. 8 events of 60 minutesClick on one of the following instances for course materials such as slides and recordings:
2015-01 2013-01 |
|
|
|
Two days of mixed lectures and hands-on sessions by SciNet analysts on how to scale up and automate your data-centric analysis using Parallel R and Parallel Python. 2 events of 420 minutesClick on one of the following instances for course materials such as slides and recordings:
2014-10 |
|
|
|
SciNet staff meets up with High School Teachers to talk about "Computational Thinking" and other topics that could be of interest to take into the class room. 6 events of 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2014-10 |
|
|
|
Learn how to create Graphical User Interfaces (GUIs) in Python with Tkinter. Please visit http://wiki.scinethpc.ca/wiki/index.php/Intro_to_Tkinter for more information. 120 minutesClick on one of the following instances for course materials such as slides and recordings:
2014-09 |
|
|
|
Half-day introductory workshop on Hadoop: quick survey of HDFS, MapReduce, Pig, and Spark. See the wiki page at http://wiki.scinethpc.ca/wiki/index.php/Hadoop_for_HPCers 180 minutesClick on one of the following instances for course materials such as slides and recordings:
2014-09 |
|
|
|
|
|
|
|
Part of the Ontario Summer School on High Performance Computing 2013 -Toronto 90 minutesClick on one of the following instances for course materials such as slides and recordings:
2013-05 2012-11 |
