|Intro to Perl||02/06/2019||3-5p||TMEC 340||User Training github||Register Here|
|MATLAB Image Processing||02/6-7/2019||8:30-4:30||TMEC 446/ TMEC L009||$500 firstname.lastname@example.org|
|Intro to O2||02/13/2019||2-4p||TMEC 306||User Training github||Register Here|
|Intro to MATLAB||02/20/2019||2-4p||TMEC 306||User Training github||Register Here|
|Intro to Python||03/06/2019||3-5p||TMEC 333||User Training github||Register Here|
|Intro to Parallel Computing||03/13/2019||3-5p||TMEC 333||User Training github||Register Here|
|Intro to O2||03/27/2019||3-5p||TMEC 333||User Training github||Register Here|
|Intro to Python||04/10/2019||3-5p||TMEC 106||User Training github||Register Here|
|Intro to R/Bioconductor||04/17/2019||3-5p||TMEC 106||R Class Files||Register Here|
|R/Biostatistics Part I||04/24/2019||3-5p||TMEC 106||Register Here (updated)|
|R/Biostatistics Part II||05/01/2019||3-5p||TMEC 306||R Biostatistics Files||Register for Part I|
|R/Biostatistics Part III||05/08/2019||3-5p||TMEC 106||R Biostatistics Files||Register for Part I|
|Intro to O2||05/15/2019||3-5p||Countway 403||User Training github||Register Here|
|Intro to Git/Github||05/22/2019||3-5p||Countway 403||User Training github||Register Here|
|Intermediate O2||05/29/2019||3-5p||Countway 403||User Training github||Register Here|
O2 for New Users addresses the needs of users who have very little linux experience, and are just getting started with HPC. More time will be devoted to covering linux basics, and the concepts of schedulers and jobs, and data management best practices. The lecture portion of this class is one hour, the second hour will be spent clinic-style with HMS RC staff to address workflow-specific questions and help convert commands to O2 SLURM syntax.
Intermediate O2 is for current O2 users who would like to brush up on their bash skills, learn more advanced file transfer techniques, and unleash some of the powerful features of the SLURM scheduler.
Python is a popular scripting language for scientific computing and available across all computer platforms. The course will introduce you to some of the basics of the Python language as well as some of the nuances involved with its use specific to the O2 environment. The goal is to provide users with a foundational level of familiarity. Topics covered include basic data types and declaration, flow control (if/else), loops, a brief introduction to constructing a script, and a briefer introduction to modules. The course will be taught on O2, but general concepts are easily translatable to desktop and local installations.
Intro to using R and Bioconductor. R is a powerful, open-source, highly adaptable statistical language useful for crunching numbers to datasets like those produced by next-gen sequencing. This class covers R basics and learning to think like/understand R. Users will learn how to set up personal R libraries on O2, and use O2 R for its high memory allocations and parallelization. Topics include how to install packages, learn about variables, data types. data manipulation, flow control, and functions, perform simple statistical tests, and create a variety of plots. Laptops are encouraged.
Class Files Here
Matlab has become the “language of science” in the past few decades. It is simple to use, yet powerful enough to be productive on large computing infrastructures. If you need: 1) Fast prototyping of research ideas; or 2) avoid spending too much time in coding instead of doing real science by taking advantage of Matlab’s built-in functions; 3) User friendly graphical interface and educational documentation; 4) Simplicity of code; 5) Easy access to GPU computing power; 6) Easy plotting and presentation of data; you will find this introduction course useful. This course will introduce the basics of the MATLAB coding language with O2-scalability and data presentation.
This is a short introduction to Parallel Computing that will include an overview of the basic concepts of parallel programming: from running your job in an embarrassingly parallel way to writing simple shared and distributed memory parallelization codes in different languages. The seminar will cover several examples of actual parallel codes however it will not have any "hands on" components. A basic programming experience (of any language, no parallelization) is preferred in order to better follow the topics presented during the seminar.
Perl is an open source programming language that's flexible, available for nearly all platforms, and is easy to learn. It is well suited for data munging, and processing biological data. Topics covered include variables (scalars, arrays and hashes), numerical and string functions, loops and conditions, regular expressions, and reading and writing files. A brief introduction will be given for subroutines and BioPerl. The class will be focused on running Perl on the O2 cluster, though the fundamentals are applicable for using Perl installations elsewhere.
This course introduces Git and GitHub and covers topics including: Getting Started with Git for version control, Using GitHub Desktop effectively, Collaborating with others on GitHub, and Utilizing GitHub Flow for better workflow. No previous exposure is assumed. We hope attendees will leave the class with the knowledge and tools necessary to start integrating Git into their workflows and excited to begin collaborating on GitHub.
R/Biostatistics is a multi-part course covering the basics of RNA-seq analysis with R. This biostatistics course covers standard supervised approaches and functional enrichment analyses of a breast cancer RNA-seq dataset. Topics include edgeR for differential analysis, GOSeq for functional enrichment analyses, and KEGG pathway analysis. Deep learning applications will be discussed. High-throughput data visualization techniques are be emphasized. Each class includes a lecture and R practicum, and registration is for all courses. Laptops are encouraged.
This two-day course provides hands-on experience with performing image analysis interactively and programmatically. Topics include:
Time: 8:30 am – 4:30 pm
To register: Contact Alyssa Silverman, email@example.com for a quote.
This seminar will be particularly valuable for anyone interested in using MATLAB to process, visualize, and quantify imagery. Rather than focus on extracting information from a few homogeneous images, we will introduce a typical real-world challenge, and discuss approaches to managing and exploring collections of widely heterogeneous images. We will also describe approaches to implementing deep learning networks in MATLAB, and will compare and contract those approaches with more computer vision and machine learning techniques.
In this presentation, we will:
* Explore and manage a range of real-world image sets
* Solve challenging image processing problems with user interfaces
* Classify images by content using machine learning techniques
* Detect, recognize, and track objects and faces in images
Are you new to deep learning and want to learn how to use it in your work? Deep learning can achieve state-of-the-art accuracy in many humanlike tasks such as naming objects in a scene or recognizing optimal paths in an environment. The main tasks are to assemble large data sets, create a neural network, to train, visualize, and evaluate different models, using specialized hardware - often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them. In this seminar, we’ll demonstrate new MATLAB features that simplify these tasks and eliminate the low-level programming. In doing so, we’ll decipher practical knowledge of the domain of deep learning. We’ll build and train neural networks that recognize handwriting, classify food in a scene, and figure out the drivable area in a city environment.
Along the way, you’ll see MATLAB features that make it easy to:
• Manage extremely large sets of images
• Visualize networks and gain insight into the black box nature of deep networks
• Perform classification and pixel-level semantic segmentation on images
• Import training data sets from networks such as GoogLeNet and ResNet
• Import and use pre-trained models from TensorFlow and Caffe
• Speed up network training with parallel computing on a cluster
• Automate manual effort required to label ground truth
• Automatically convert a model to CUDA to run on GPUs