{ This is a handson workshop in pgmpy package. The creator of pgmpy package Abinash Panda will do the code demonstration }

Crude oil plays an important role in the macroeconomic stability and it heavily influences the performance of the global financial markets. Unexpected fluctuations in the real price of crude oil are detrimental to the welfare of both oil-importing and oil-exporting economies.Global macro hedge-funds view forecast the price of oil as one of the key variables in generating macroeconomic projections and it also plays an important role for policy makers in predicting recessions.

Probabilistic Graphical Models can help in improving the accuracy of existing quantitative models for crude oil price prediction as it takes in to account many different macroeconomic and geopolitical variables .

Hidden Markov Models are used to detect underlying regimes of the time-series data by discretising the continuous time-series data. In this workshop we use Baum-Welch algorithm for learning the HMMs, and Viterbi Algorithm to find the sequence of hidden states (i.e. the regimes) given the observed states (i.e. monthly differences) of the time-series.

Belief Networks are used to analyse the probability of a regime in the Crude Oil given the evidence as a set of different regimes in the macroeconomic factors . Greedy Hill Climbing algorithm is used to learn the Belief Network, and the parameters are then learned using Bayesian Estimation using a K2 prior. Inference is then performed on the Belief Networks to obtain a forecast of the crude oil markets, and the forecast is tested on real data.

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.

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 and Keras.

Given we have only 8 hours, we will cover the most important fundamentals,
current techniques and avoid anything which is obsolete or not being used by
state-of-art algorithms. We will directly start with building the intuition for
Convolutional Neural Networks, and focus on core architectural problems. We
will try and answer some of the hard questions like how many layers must be
there in a network, how many kernels should we add. We will look at the
architectural journey of some of the best papers and discover what each brought
into the field of Vision AI, making today’s best networks possible. We will cover 9
different kinds of Convolutions which will cover a spectrum of problems like
running DNNs on constrained hardware, super-resolution, image segmentation,
etc. The concepts would be good enough for all of us to move to harder problems
like segmentation or super-resolution later, but we will focus on object
recognition, followed by object detections. We will build our networks step by
step, learning how optimizations techniques actually improve our networks and
exactly when should we introduce them. We hope the leave you in confidence
which will help you read research papers like your second nature. Given we have
8 hours, and we want the sessions to be productive, we will instead of introducing

all the problems and solutions, focus on the fundamentals of modern deep neural
networks.

The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).

With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

Precision of these analyses have become vital and way forward for disease detection, its predisposition, empowers medical authorities to make fair and situationally decision about patient treatment strategies. This kind of genomic profiling, prediction and mode of disease management is useful to tailoring FDA approved treatment strategies based on these molecular disease drivers and patient’s molecular makeup.

Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

{ This is a handson workshop . The use case is Traffic analysis . }

Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein edges represent the conditional probability factor in the factorized probability distribution. Thus Bayesian Networks provide a compact representation for dealing with uncertainty using an underlying graphical structure and the probability theory. These models have a variety of applications such as medical diagnosis, biomonitoring, image processing, turbo codes, information retrieval, document classification, gene regulatory networks, etc. amongst many others. These models are interpretable as they are able to capture the causal relationships between different features .They can work efficiently with small data and also deal with missing data which gives it more power than conventional machine learning and deep learning models.

In this session, we will discuss concepts of conditional independence, d- separation , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.

Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.