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. Location: SciNet Online

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. Location: SciNet Online

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. Location: SciNet Online

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. Location: SciNet Online

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. Location: SciNet Online

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. Location: SciNet Online

Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online

Parallel programming in Python with a focus on parallel data analysis. We will cover subprocess, multiprocessing and other parallel-enabling python packages. Location: SciNet Online

Learn how to model the neural network in the brain using Python. Location: SciNet Online

Learn how to model the neural network in the brain using Python. Location: SciNet Online

Learn parallel programming R, with a focus on parallel data analysis. Location: SciNet Online

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. Location: SciNet Online

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. Location: SciNet Online

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. Location: SciNet Online

This course is to introduce graduate students to the programing language Python. The course will teach the students how to install Python on their laptop and then use Python to perform data analysis. The course consists of twelve hands-on sessions, each lasting one hour, where students bring their own laptops and perform assignments, each of these assignments being due for the following lecture. Part of BCH2024H Introduction to Programming in Python

This summer school will familiarize the best students in computational sciences with major state-of-the-art aspects of HPC for a variety of scientific disciplines, catalyze the formation of networks, provide advanced mentoring, facilitate international exchange and open up further career options. Leading Canadian, European, Japanese and American computational scientists and HPC technologists will offer instruction in parallel sessions on a variety of topics as: HPC challenges in major scientific disciplines, HPC programming proficiencies, Performance analysis and profiling, Software engineering, Numerical libraries, Big data analysis and analytics, Machine learning, Scientific visualization, and Canadian, European, Japanese and US HPC infrastructure. This events was originally planned to take place in July 2020 but has been moved to July 2021 instead.