This is a mixer workshop with lot of clinicians , medical experts , Neuroimaging experts ,Neuroscientists, data scientists and statisticians will come under one roof to bring together this revolutionary workshop.

The theme will be updated soon .

Our celebrity and distinguished presenter Srikanth Ramaswamy who is an advisor at Mysuru Consulting Group and also works Blue Brain Project at the EPFL will be delivering an expert talk in the workshop.

https://www.linkedin.com/in/ramaswamysrikanth/

{ This workshop will be a combination of panel discussions , expert talk and neuroimaging data science workshop ( applying machine learning and deep learning algorithms to Neuroimaging data sets}

{ We are currently onboarding several experts from Neuroscience domain --Neurosurgeons , Neuroscientists and Computational Neuroscientists .Details of the speakers will be released soon }

Abstract for the Neuroimaging Data Science Part of the workshop:

The study of the human brain with neuroimaging technologies is at the cusp of an exciting era of Big Data. Many data collection projects, such as the NIH-funded Human Connectome Project, have made large, high- quality datasets of human neuroimaging data freely available to researchers. These large data sets promise to provide important new insights about human brain structure and function, and to provide us the clues needed to address a variety of neurological and psychiatric disorders. However, neuroscience researchers still face substantial challenges in capitalizing on these data, because these Big Data require a different set of technical and theoretical tools than those that are required for analyzing traditional experimental data. These skills and ideas, collectively referred to as Data Science, include knowledge in computer science and software engineering, databases, machine learning and statistics, and data visualization.

The workshop covers Data analysis, statistics and data visualization and applying cutting-edge analytics to complex and multimodal neuroimaging datasets . Topics which will be covered in this workshop are statistics, associative techniques, graph theoretical analysis, causal models, nonparametric inference, and meta-analytical synthesis.

 
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Outline/Structure of the Workshop

{ The agenda for the panel discussions , expert talk part of the workshop will be released soon}

The below is the outline for Neuroimaging Data Science part of the workshop:

1)Basics of Neuroimaging

2)Introduction to Functional Connectivity

  1. Extracting times series to build a functional connectome
  2. Single subject maps of seed-to-voxel correlation
  3. Single subject maps of seed-to-seed correlation
  4. Group analysis of resting-state fMRI with ICA (CanICA)

2)Introduction to Diffusion Imaging

  1. Reconstruction of the diffusion signal with the Tensor model
  2. Other reconstruction approaches, such as sparse fascicle models (SFM)
  3. Introduction to Tractography
  4. Using Various Tissue Classifiers for Tractography
  5. Connectivity Matrices, ROI Intersections, and Density Maps
  6. Direct Bundle Registration

3) Data Preparation

4) Manipulation of data using nilearn and nilabel

5)Data Visualization

6)Modelling using Machine Learning Algorithms

7)Modelling using Deep Learning (Keras package)

Implementing a CNN to classify neuroimages, in our case fMRI images.

Learning Outcome

The agenda will be updated soon

Target Audience

Neuroscientists, Neuroimaging specialist, Neuropathologists, Data Scientists, Medical Researchers, Machine Learning Engineers, Deep Learning Engineers, Computational Biologists.

Prerequisites for Attendees

Basic Understanding of Machine Learning and Deep Learning

Familiarity with Neurimaging datasets preferred , though not mandatory

schedule Submitted 1 month ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  2 weeks ago
    reply Reply

    Dear Usha: Please add specific learning outcomes for this proposal.

    What do the attendees take away?

    What is the panel discussion about?

    What is the expert talk about?

    Warm Regards, Vikas


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    Intermediate

    Computer Vision has lots of applications including medical imaging, autonomous vehicles, industrial inspection and augmented reality. Use of Deep Learning for computer Vision can be categorized into multiple categories for both images and videos – Classification, detection, segmentation & generation.

    Having worked in Deep Learning with a focus on Computer Vision have come across various challenges and learned best practices over a period experimenting with cutting edge ideas. This workshop is for Data Scientists & Computer Vision Engineers whose focus is deep learning. We will cover state of the art architectures for Image Classification, Segmentation and practical tips & tricks to train a deep neural network models. It will be hands on session where every concepts will be introduced through python code and our choice of deep learning framework will be PyTorch v1.0.

    The workshop takes a structured approach. First it covers basic techniques in image processing and python for handling images and building Pytorch data loaders. Then we introduce how to build image classifier followed by how segmentation was done in pre CNN era and cover clustering techniques for segmentation. Start with basics of neural networks and introduce Convolutional neural networks and cover advanced architecture – Resnet. Introduce the idea of Fully Convolutional Paper and it’s impact on Semantic Segmentation. Cover latest semantic segmentation architecture with code and basics of scene text understanding in pytorch with how to run carefully designed experiments using callbacks, hooks. Introduce discriminative learning rate and mixed precision to train deep neural network models. Idea is to bridge the gap between theory and practice and teach how to run practical experiments and tune deep learning based systems by covering tricks introduced in various research papers. Discuss in-depth on the interaction between batchnorm, weight decay and learning rate.

  • Liked Ramanathan R
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    Ramanathan R / Gurram Poorna Prudhvi - Time Series analysis in Python

    480 Mins
    Workshop
    Intermediate

    “Time is precious so is Time Series Analysis”

    Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

    Structure of the workshop goes like this

    • Basics of time series analysis
    • Understanding Time series data with pandas
    • Preprocessing Time Series data
    • Classical Time series models (AR, MA, ARMA, ARIMA, SARIMA, GARCH, E-GARCH)
    • Forecasting with MLP (Multi-Layer Perceptron)
    • Forecasting with RNN (Recurrent Neural Network)
    • Forecasting with LSTM (Long Short Term Memory Network)
    • Understanding Financial Time Series data and forecasting with RNN and LSTM
    • Boosting techniques in Time series data
    • Developing intuition to choose the right network.
    • Dealing with large scale Time Series data



    Libraries Used:

    • Keras (with Tensorflow backend)
    • matplotlib
    • pandas
    • statsmodels
    • prophet
    • pyflux
    • tsfresh
  • Liked Amit  Baldwa
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    Amit Baldwa - PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE LEARNING AND TECHNICAL ANALYSIS

    45 Mins
    Demonstration
    Intermediate

    Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

    Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

    Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

    We will evaluate whether stock returns can be predicted based on historical information.

    Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value