ODSC West Schedule 2019

Bootcamp | Kickstart | Beginner

The workhorse of many Machine Learning and Deep Learning applications is gradient descent. There are many different flavors and implementations of gradient descent algorithms, but they all serve the same goal: optimize the output of a model given an objective function. Implementations exist in popular libraries and APIs that data scientists and researchers use daily, such as PyTorch, Sklearn, and Tensorflow.

Considering there are many implementations in Python, we often take these algorithms for granted. In this session, we’ll walk through the fundamentals of gradient descent algorithms, supervised machine learning, and work on fundamentals of building objects in Python. Even if you never implement your own gradient descent algorithm, knowing the foundational tools and techniques to create one is critical to being a successful data scientist.

By the end of this session, you’ll be able to construct classes in Python that function as basic machine learning models using various gradient descent algorithms…more details

Training | Open Source | Beginner-Intermediate

Our goal is for every data scientist to practice data ethics. In this workshop, you will learn how to make data ethics actionable as a data scientist.

For this workshop, we’ll use the resources collected in deon (http://deon.drivendata.org), an open source command line tool that integrates an ethics checklist into your existing data science workflow. The goal of deon is to enable teams to flexibly carry out the ethical discussions most relevant to them, and to preemptively address issues they may overlook. Instead of relying on an “Ultimately True” philosophy or oath, deon encourages an upfront and ongoing dialogue about the different ethical aspects of your project. This dialogue will span a broad set of ethical issues that frequently arise in machine learning and data science contexts. Specific solutions often vary with the task at hand, but deon helps cultivate ethical intentionality for the first line of defense––the engineers that have an influence over how data science actually gets done…more details

Half-Day Training | Data Visualization | Machine Learning | Intermediate

This tutorial will cover the state-of-the-art research, development, and applications in the KDD area of interpretable knowledge discovery reinforced by visual methods to stimulate and facilitate future work. It will serve the KDD mission of gaining insight from data. The topic is interdisciplinary bridging scientific research and applied communities in KDD, Visual Analytics, Information Visualization, and HCI. This is a novel and fast growing area with significant applications, and potential.

First, in KDD, these studies have grown under the name of visual data mining. The recent growth under the names of deep visualization, and visual knowledge discovery, is motivated considerably by deep learning success in accuracy of prediction and its failure in explanation of produced models without special interpretation efforts. In the areas of Visual Analytics, Information Visualization, and HCI, the increasing trend toward machine learning tasks, including deep learning, is also evident…more details

Workshop | Open Source | Machine Learning | Beginner-Intermediate

Gradient boosting is a machine-learning technique that achieves state-of-the-art results in a variety of practical tasks. For a number of years, it has remained the primary method for learning problems with heterogeneous features, noisy data, and complex dependencies: web search, recommendation systems, weather forecasting, and many others.

CatBoost (http://catboost.ai) is one of the three most popular gradient boosting libraries. It has a set of advantages that differentiate it from other libs…more details

Training | Machine Learning | Intermediate

Many industry segments have been grappling with fast data (high-volume, high-velocity data). The enterprises in these industry segments need to process this fast data just in time to derive insights and act upon it quickly. Such tasks include but are not limited to enriching data with additional information, filtering and reducing noisy data, enhancing machine learning models, providing continuous insights on business operations, and sharing these insights just in time with customers. In order to realize these results, an enterprise needs to build an end-to-end data processing system, from data acquisition, data ingestion, data processing, and model building to serving and sharing the results. This presents a significant challenge, due to the presence of multiple messaging frameworks and several streaming computing frameworks and storage frameworks for real-time data. In the proposed tutorial, we shall lead a journey through the landscape of state-of-the-art systems for each stage of an end-to-end data processing pipeline, messaging frameworks, streaming computing frameworks, storage frameworks for real-time data, and more…more details

Tutorial | Deep Learning | Machine Learning

Prediction rules in deep learning are based on a forward, recursive computation through several layers. Implicit rules go much beyond, by relying on the solution of a fixed-point equation that has to be numerically solved, in order to make the prediction. The equation involves a single n-vector x that contains the “hidden” features.

At first glance, the above model class seems very specific. Perhaps surprisingly, we can cast most known neural network architectures, including standard feedforward networks, CNNs, RNNs, and many more. Implicit rules allow for much wider classes of models, with a lot more capacity (number of parameters for a given dimension of the hidden features)…more details

Training | Open Source | All levels

If you’ve never heard of the “good, fast, cheap” dilemma, it goes something like this: You can have something good and fast, but it won’t be cheap. You can have something good and cheap, but it won’t be fast. You can have something fast and cheap, but it won’t be good. In short, you can pick two of the three but you can’t have all three.

If you’ve done a data science problem before, I can all but guarantee that you’ve run into missing data. How do we handle it? Well, we can avoid, ignore, or try to account for missing data. The problem is, none of these strategies are good, fast, *and* cheap…more details

Training | Machine Learning | Intermediate

How can an idea from an 18th century Presbyterian minister be used to estimate the mass density function of galaxies across the Universe? How can a marketing segmentation algorithm protect astronauts traveling to Mars from certain death? How does a Formula 1 race from then 1950’s inspire one of the greatest data science use cases for the Internet of Things? How can a violation of the triangle inequality theorem in mathematics lead to a cure for cancer? This workshop will answer these questions, and more, by presenting several examples of one of the key aptitudes of successful data science practice, which is adaptability. In particular, I will present several well known algorithms (including some that we would not even call “algorithms”) that may have been adopted for specific use cases or applied in specific business domains…more details

Training | Kickstarter | Data Visualization | Beginner – Intermediate

Markdown Primer (45 minutes): Structure Documents with Sections and Subsections, Formatting Text, Creating Ordered and Unordered Lists, Making Links, Number Sections, Include Table of Contents
Integrate R Code (30 minutes): Insert Code Chunks, Hide Code, Set Chunk Options, Draw Plots, Speed Up Code with Caching
Build RMarkdown Slideshows (20 minutes): Understand Slide Structure, Create Sections, Set Background Images, Include Speaker Notes, Open Slides in Speaker Mode
Develop Flexdashboards (30 minutes): Start with the Flexdashboard Layout, Design Columns and Rows, Use Multiple Pages, Create Social Sharing, Include Code
Shiny InputsDrop DownsTextRadioChecks
Shiny OutputsTextTablesPlots
Reactive Expressions
HTML WidgetsInteractive PlotsInteractive MapsInteractive Tables
Shiny Layouts UI and Server Files User Interface…more details

Training | Kickstarter | Beginner

For the Python Fundamentals session, we will start by ensuring we have Jupyter Notebook installed and then jump right into Python. It will be a fast-paced crash course, covering the basics from data types and looping operations to more intermediate topics such as lambda expressions and object-oriented programming. By the end of it, you will feel comfortable with Python syntax, and be able to use Python code to solve data analysis and mathematical problems that will form the foundation for more advanced sessions…more details

Training | Deep Learning | Open Source | Beginner-Intermediate

Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing.

This Deep Learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2.0, the major, cutting-edge revision of the world’s most popular Deep Learning library.

To facilitate an intuitive understanding of Deep Learning’s artificial-neural-network foundations, the essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on Python code run-throughs provided in straightforward Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art Deep Learning models…more details

Training | Data Visualization | Beginner-Intermediate

The wealth of data available offers unprecedented opportunities for discovery and insight. How do we design a more intuitive and useful data experience? This workshop focuses on approaches to turn data into actionable insights by combining principles from data visualization and user experience design. Participants will be asked to think holistically about data visualizations and the people they serve. Through presentations and hands-on exercises, participants will learn how to choose and create data visualizations driven by user-oriented objectives…more details

Training | AI for Engineers | Open Source | Intermediate-Advanced

Whether in R, MATLAB, Stata, or Python, modern data analysis, for many researchers, requires some kind of programming. The preponderance of tools and specialized languages for data analysis suggests that general purpose programming languages like C and Java do not readily address the needs of data scientists; something more is needed.

In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for interactive data analysis. Pandas is a massive library, so we will focus on its core functionality, specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals of Pandas, be aware of common pitfalls, and be ready to perform your own analyses…more details

Training | Data Visualization | DevOps & Management | Intermediate

Humans evolved to tell stories. In fact, we evolved to rely on story for our most important learning. Some argue story was more important for the survival of the species than opposable thumbs.

In this half-day workshop, learn how to take a step back from your data and think like a storyteller. Learn some key ideas and techniques to turn your numbers into a narrative – to make a compelling story that will have an impact on your audience. We will cover practical, actionable ideas that will make your next effort at communicating with data much more powerful…more details

Training | Machine Learning | Data Visualization | Beginner-Intermediate

Literate computing weaves a narrative directly into an interactive computation. Text, code, and results are combined into a narrative that relies equally on textual explanations and computational components. Insights are extracted from data using computational tools. These insights are communicated to an audience in the form of a narrative that resonates with the audience. Literate computing lends itself to the practice of reproducible research. One may re-run the analyses; run the analyses with new data sets; modify the code for other purposes.

This workshop will take one through the steps associated with literate computing: data retrieval; data curation; model construction, evaluation, and selection; and reporting. Particular attention will be paid to reporting, i.e., building a narrative. Examples will be presented demonstrating how one might generate multiple output formats (e.g., HTML pages, presentation slides, PDF documents) starting with the same code base…more details

Training | DevOps & Management | Open Source | Beginner-Intermediate

Comparing the similarity of two sentences is an integral part of many Natural Language Processing scenarios. These scenarios range from search and retrieval, nearest-neighbor to kernel-based classification methods, recommendation, and ranking tasks. Building state of the art models at production level scale can be difficult when you’re on a small team and not both an NLP and DevOps expert. In this workshop, we will walk through the Natural Language Processing Best Practices Github Repo (https://github.com/microsoft/nlp ) provided by Microsoft on how to create baseline representation models for Sentence Similarity scenarios from popular open source technologies like gensim and scikit-learn. We will then use Microsoft’s Automated Machine Learning to create a competitive model with popular sentence encoders from Google and create reusable machine learning pipelines deployed at scale on Azure Kubernetes Services…more details

Training | Kickstarter | Machine Learning | All Levels

Scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some of the more advanced aspects of scikit-learn, such as building complex machine learning pipelines, advanced model evaluation, feature engineering and working with imbalanced datasets. We will also work with text data using the bag-of-word method for classification.

This workshop assumes familiarity with Jupyter notebooks and basics of pandas, matplotlib and numpy. It also assumes some familiarity with the API of scikit-learn and how to do cross-validations and grid-search with scikit-learn…more details

Training | Deep Learning | Research Frontiers | Intermediate

In this tutorial, we present deep learning methods and their applications in computational healthcare, specifically focusing on clinical representation learning, predictive modeling, clinical trial modeling and drug development.
We will also introduce different types of data in healthcare including structured electronic health records, unstructured clinical notes, medical images, clinical trial description, chemical compounds and medical knowledge base.
This tutorial is intended for data scientists, engineers and researchers who are interested in applying deep learning methods to healthcare, and prerequisite knowledge include basic machine knowledge. The first half will be spent on introducing the nature of health data, basic deep learning methods and their application in healthcare. In the second half, we will focus on challenges specific to computational healthcare, and introduce advanced deep learning methods ranging from deep phenotyping, chronic disease prediction, rare disease detection, patient-trial matching, and molecule generation and drug property prediction. The tutorial will be concluded with open problems and a Q&A session…more details

Training | Deep Learning | Open Source | Intermediate

Using articles published on the NASDAQ website as a data source, a search engine that gives direct answers to naturally phrased questions will be built by attendees. An example query might be, ‘What market does FitBit compete in?’, and the expected output for this query would be something akin to: ‘wearables’.

To accomplish this task, Python and the open-source libraries Whoosh and DeepPavlov will be used as the main tools to build the search engine. Whoosh will be used to build a more traditional text-based search engine, and a pre-trained Deep Learning model from DeepPavlov will be used to perform natural language Q&A with the documents returned by Whoosh.

While covering Whoosh there will be a general discussion of information retrieval and deeper dives into the NLP tasks being performed by Whoosh. These techniques include tokenization, stopword removal, n-grams, stemming, lemmatization, and Named Entity Recognition…more details

Training | Data Visualization | Open Source | Intermediate

Shiny is an R package that can be used to build interactive web pages with R. This might sound strange or scary, but you don’t need to have any web knowledge – it’s just R! If you’ve ever written an analysis in R and you want to make it interactive, you can use Shiny. If you’ve ever written a function or model that you want to share with others who don’t know how to use R, you can use Shiny. Shiny has many use cases, and this course will help you see how you can leverage it in your own work. In this workshop, you’ll learn how to take a Shiny app from start to finish – we’ll start by building a simple Shiny app to interactively visualize a dataset, and deploy it online to make it accessible to the world…more details

Workshop | Machine Learning | Intermediate

In this workshop we will dive deep into what it takes to build and deliver an always-on “real-ish time” predictive analytics pipeline with Spark Structured Streaming.

The core focus of the workshop material will be on how to solve a common complex problem in which we have no labeled data in an unbounded timeseries dataset and need to understand the substructure of said chaos in order to apply common supervised and statistical modeling techniques to our data in a streaming fashion.

The example problem for the workshop will come from the telecommunications space but the skills you will leave with can be applied to almost any domain as long as you sprinkle in a little creativity and inject a bit of domain knowledge…more details

Training | Kickstarter | Machine Learning | All Levels

Modern statistics has become almost synonymous with machine learning, a collection of techniques that utilize today’s incredible computing power. This two-part course focuses on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theory behind the curtain. We start with the foundation of it all, the linear model and its generalization, the glm. We look how to assess model quality with traditional measures and cross-validation and visualize models with coefficient plots. Next we turn to penalized regression with the Elastic Net. After that we turn to Boosted Decision Trees utilizing xgboost. Attendees should have a good understanding of linear models and classification and should have R and RStudio installed, along with the `glmnet`, `xgboost`, `boot`, `ggplot2`, `UsingR` and `coefplot` packages…more details

Training | Deep Learning | Open Source | Beginner – Intermediate

In this session, we will cover how to create Deep Neural Networks using the PyTorch framework on a variety of examples. The material will range from beginner – understanding what is going on “”under the hood””, coding the layers of our networks, and implementing backpropagation – to more advanced material on RNNs,CNNs, LSTMs, & GANs.

Attendees will leave with a better understanding of the PyTorch framework. In particular, how it differs from Keras and Tensorflow. Furthermore, a link to a clean documented GitHub repo with the solutions of the examples covered will be provided…more details

Training | Deep Learning | AI for Engineers | Intermediate-Advanced

We will build and tweak several vision classifiers together starting with perceptrons and building up to transfer learning and convolutional neural networks. We will investigate practical implications of tweaking loss functions, gradient descent algorithms, network architectures, data normalization, data augmentation and so on. This class is super hands on and practical and requires no math or experience with deep learning…more details

Training | Kickstarter | Machine Learning | All Levels

Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detecting the Higgs boson, machine learning excells at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to “”learn”” a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formalizing a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. The talk should give you all the necessary background to start using machine learning yourself…more details

Training | Deep Learning | Intermediate-Advanced

This is a very hands on introduction to LSTMs in Keras and TensorFlow. We will build a language classifier, generator and a translating sequence to sequence model. We will talk about debugging models and explore various related architectures like GRUs, Bidirectional LSTMs, etc. to see how well they work…more details

Training | Machine Learning | Open Source | Beginner-Intermediate

We’ll start with the basics of machine learning on Apache Spark: when to use it, how it works, and how it compares to all of your other favorite data science tooling.

You’ll learn to use Spark (with Python) for statistics, modeling, scoring (inference), and model tuning. But you’ll also get a peek behind the APIs: see why the pieces are arranged as they are, how to get the most out of the docs, open source ecosystem, third-party libraries, and solutions to common challenges…more details

Tutorial | Deep Learning | Open Source | Advanced

We will cover new, exciting, unconventional techniques for improving population-based search. These ideas are already enabling us to solve hard problems. They also hold great promise for further advancing machine learning, including deep neural networks. Major topics covered include (1) explicitly searching for behavioral diversity (in a low-dimensional space where diversity is inherently interesting, such as the behavior of robots, rather than in the true search space, such as the weights of the DNN that controls the robot), especially Quality Diversity algorithms, which have produced state-of-the-art results in robotics and solved a version of the hard-exploration RL challenge of Montezuma’s Revenge; (2) open-ended search, wherein algorithms continually create new and increasingly complex capabilities without bound, for example by simultaneously inventing new challenges and their solutions; and (3) indirect encoding (e.g. HyperNEAT/HyperNetworks), wherein one network encodes how to construct a larger neural network or learning system…more details

Workshops | Deep Learning | Intermediate

Deep Learning became known for beating human performance on image classification, and most applications and published examples for deep learning still focus on image processing. However, deep learning nowadays can also be applied successfully to time-series (or sensor) data and text. In this hands-on MATLAB workshop, you will see how easy it is to get started applying deep learning to various data types, including images, text, and time-series data. You’ll use an online MATLAB instance to perform the following tasks:
1. Train deep neural networks on GPUs in the cloud
2. Access and explore pretrained models
3. Build a CNN to solve an image classification problem
4. Use LSTM networks to solve a time-series and text analytics problem…more details

Workshops | Research Frontiers

Thanks to advances in imitation and reinforcement learning techniques, we can now train intelligent agents to accomplish a diverse range of goals. But if we want to create household robots or personal assistants that can take advantage of this diversity, we need to give users some way to tell them what to do! This tutorial will focus on humans’ favorite tool for communicating goals and plans: natural language. We’ll assume basic familiarity with supervised learning and RL, and begin with a review of core machine learning techniques useful for natural language instruction following problems. The body of the talk will focus on modeling techniques for instruction following problems in different kinds of environments and data conditions. We’ll conclude with a survey of other applications for the tools we’ve built, including instruction generation, interpretability, and machine teaching…more details

Workshop | Data Visualization | Open Source | Intermediate

In this hands-on workshop, we will use R to take public data from various sources and combine them to find statistically interesting patterns and display them in static and dynamic, web-ready maps. This session will cover topics including geojson and shapefiles, how to munge Census Bureau data, geocoding street addresses, transforming latitude and longitude to the containing polygon, and data visualization principles.

Participants will leave this workshop with a publication-quality data product and the skills to apply what they’ve learned to data in their field or area of interest…more details

Training | Deep Learning | Machine Learning | Beginner – Intermediate

In this workshop you will discover how machines can learn complex behaviors and anticipatory actions. Using this approach autonomous helicopters fly aerobatic maneuvers and even the GO world champion was beaten with it. A training dataset containing the “right” answers is not needed, nor is “hard-coded” knowledge. The approach is called “reinforcement learning” and is almost magical.

Using TF-Agents on top of TensorFlow 2.0 we will see how a real-life problem can be turned into a reinforcement learning task. In an accompanying Python notebook, we implement – step by step – all solution elements, highlight the design of Google’s newest reinforcement learning library, point out the role of neural networks and look at optimization opportunities…more details

Tutorial | Machine Learning | Open Source | Beginner-Intermediate

An important practical challenge is to develop theoretically-principled tools that can be used to guide the use of production-scale deep neural networks. We will describe recent work that has focused on using spectral-based methods from scientific computing and statistical mechanics to develop such tools. Among other things, these tools can be used to develop metrics characterizing the quality of models, without even examining training or test data; and they can be used to predict trends in generalization (and not just bounds on generalization) for state-of-the-art production-scale models. Related tools can be used to exploit adversarial data to characterize and modify the curvature properties of the penalty landscape and to perform tasks such as model quantization in a more automated way…more details

Training | Kickstarter | Beginner

First Hour
i. Installation of Anaconda/Postgresql
ii. Quick review?
iii. File Handling
iv. String formatting?
v. Built in libraries to do…more details

Training | AI for Engineers | DevOps & Management | Beginner-Intermediate

Container have emerged as an excellent tool for building portable and scalable applications, with Kubernetes as a leading system for orchestrating container deployments across clusters. However, in order for data science teams to reap the benefits of Kubernetes for machine learning they must first overcome myriad engineering and DevOps challenges.

Kubeflow seeks to simplify the process of building, deploying, and scaling machine learning workflows on Kubernetes. Originally an internal project at Google for streamlining TensorFlow jobs on Kubernetes but has since been open sourced and now supports a variety of frameworks and workflows. Kubeflow takes care of many common pain points, allowing data science teams to focus on building and deploying models instead of managing a brittle network of systems held together with glue code…more details

Training | Machine Learning | Open Source | All Levels

Scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This training will cover some advanced topics in using scikit-learn, such as how to perform out-of-core learning with scikit-learn and how to speed up parameter search. We’ll also cover how to build your own models or feature extraction methods that are compatible with scikit-learn, which is important for feature extraction in many domains. We will see how we can customize scikit-learn even further, using custom methods for cross-validation or model evaluation.

This workshop assumes familiarity with Jupyter notebooks and basics of pandas, matplotlib and numpy. It also assumes experience using scikit-learn and familiarity with the API…more details

Workshop | Deep Learning | Open Source | All Levels

This workshop shows how interpretability tools can give you not only more confidence in a model, but also help to improve model performance. Through this interactive workshop, you will learn how to better understand the models you build, along with the latest techniques and many tricks of the trade around interpretability. The workshop will largely focus on interpretability techniques, such as feature importance, partial dependence, and explanation approaches, such as LIME and Shap.
The workshop will demonstrate interpretability techniques with notebooks, some in R and some in Python. Along the way, workshop will consider issues like spurious correlation, random effects, multicollinearity, reproducibility, and other issues that may affect model interpretation and performance. To illustrate the points, the workshop will use easy to understand examples and references to open source tools to illustrate the techniques…more details

Training | Open Source | DevOps & Management | Beginner – Intermediate

The intention of this VERY hands on workshop is to get you introduced and playing with some of the great features you never knew about in PostgreSQL. You know, and probably already love, PostgreSQL as your relational database. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will add in some nice statistical work with R embedded in PostgreSQL. Finally we will bring this all together using the gold standard in spatial databases, PostGIS. Unless you have a specialized use case, PostgreSQL is the answer. The session will be very hands on with plenty of interactive exercises.

By the end of the workshop participants will leave with hands on experience doing Spatial Analysis, JSON search, Full Text Search , Using R for stored procedures and functions, All in PostgreSQL…more details

Training | DevOps & Management | Machine Learning | Intermediate

ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.

Jules Damji walks you through MLflow, an open source project that simplifies the entire ML lifecycle, to solve this problem. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.

What you’ll learn
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, and MLflow Models) and how each help address challenges of the ML lifecycle…more details

Training | AI for Engineers | Deep Learning | All levels

Powering your application with deep learning is no walk in the park, but is certainly attainable with some tricks and good practice. Serving a deep learning model on a production system demands the model to be stable, reproducible, capable of isolation and behave as a stand-alone package. One possible solution to this is a containerized microservice.

Ideally, serving deep learning microservices should be quick and efficient, without having to dive deep into the underlying algorithms and their implementation. Too good to be true? Not anymore! Together, we will demystify the process of developing, training, and deploying deep learning models as a web microservice.

We will kick off with an overview of how deep learning models are best published as Docker images on DockerHub, and are best prepared for deployment in local or cloud environments using Kubernetes or Docker…more details

Training | Machine Learning | Open Source | Beginner – Intermediate

Scikit-learn is a machine learning library in Python, that has become a valuable tool for many data science practitioners. This talk will cover some of the more advanced aspects of scikit-learn, such as building complex machine learning pipelines, model evaluation, parameter search, and out-of-core learning. Apart from metrics for model evaluation, we will cover how to evaluate model complexity, and how to tune parameters with grid search, randomized parameter search, and what their trade-offs are. We will also cover out of core text feature processing via feature hashing…more details

Half-Day Training

As data processing becomes more real time, stream processing is becoming more important. Apache Flink makes it easier to build and manage stream processing applications. Flink’s new SQL interface is a great way to get started with Flink—and to build and maintain production applications.

Seth Wiesman offers an overview of Apache Flink via the SQL interface, covering stream processing and Flink’s various modes of use. Then you’ll use Flink to run SQL queries on data streams and contrast this with the Flink DataStream API…more details

Half-Day Training

Understanding the questions posed by instructors and students alike plays an important role in the development of educational technology applications. In this intermediate level workshop, you will learn to apply NLP to one piece of this real-world problem by building a model to predict the type of answer (e.g. entity, description, number, etc.) a question elicits. Specifically, you will learn to:
1. Perform preprocessing, normalization, and exploratory analysis on a question dataset,
2. Identify salient linguistic features of natural language questions, and
3. Experiment with different feature sets and models to predict the answer type.

The concepts will be taught using popular NLP and ML packages like SpaCy, Scikit Learn, and Tensorflow…more details

Training

What separates the world’s best data visualizations from the rest? Join Metis Senior Data Scientist Jonathan Balaban as he highlights amazing visuals and dissects best practices in visualization and storytelling. Then, he will guide you through open-source platforms and code that enable similar, compelling visuals we can use to share our work.
Outline:
1. Introduce five impactful and dynamic visualizations
Discuss use, design choices, and pros/cons of our visualizations
Review which domains or data are best suited for our visualizations
Familiarization with dataset and quick visualization as baseline
2. Introduce packages for build, and walk through each visualization’s codeset
Review default arguments and method choices
Consider alternatives and when they would be appropriate
3. Discuss sharing and deployment of results
Tips and tools for embedding into front ends
Review best practices from Tufte and industry experts…more details

Workshop | Machine Learning | Data Visualization | Beginner – Intermediate

Working with mathematicians, data scientists, and domain experts at the University of Vermont Complex Systems Center, data visualization artist Jane Adams has developed strategies for prototyping exploratory graphs of high-dimensional data. In this 90-minute workshop, Adams shares some of these methods for data discovery and interaction, navigating a creative workflow from paper prototypes of visual hypotheses through web-based interactive slices, offering critical insight for clustering, interpolation, and feature engineering…more details

Training | Data Visualization | Open Source | Intermediate

Markdown Primer (45 minutes): Structure Documents with Sections and Subsections, Formatting Text, Creating Ordered and Unordered Lists, Making Links, Number Sections, Include Table of Contents
Integrate R Code (30 minutes): Insert Code Chunks, Hide Code, Set Chunk Options, Draw Plots, Speed Up Code with Caching
Build RMarkdown Slideshows (20 minutes): Understand Slide Structure, Create Sections, Set Background Images, Include Speaker Notes, Open Slides in Speaker Mode
Develop Flexdashboards (30 minutes): Start with the Flexdashboard Layout, Design Columns and Rows, Use Multiple Pages, Create Social Sharing, Include Code
Shiny InputsDrop DownsTextRadioChecks
Shiny OutputsTextTablesPlots
Reactive Expressions
HTML WidgetsInteractive PlotsInteractive MapsInteractive Tables
Shiny Layouts UI and Server Files User Interface…more details

Training | Data Visualization | Open Source | Intermediate

Have you ever wondered about how those data scientists at Facebook and LinkedIn make friend recommendations? Or how epidemiologists track down patient zero in an outbreak? If so, then this tutorial is for you. In this tutorial, we will use a variety of datasets to help you understand the fundamentals of network thinking, with a particular focus on constructing, summarizing, and visualizing complex networks.

This tutorial is for Pythonistas who want to understand relationship problems – as in, data problems that involve relationships between entities. Participants should already have a grasp of for loops and basic Python data structures (lists, tuples and dictionaries). By the end of the tutorial, participants will have learned how to use the NetworkX package in the Jupyter environment, and will become comfortable in visualizing large networks using Circos plots. Other plots will be introduced as well…more details

Training | Deep Learning | Intermediate

Deep learning is the newest area of machine learning and has become ubiquitous in predictive modeling. The complex, brainlike structure of deep learning models is used to find intricate patterns in large volumes of data. These models have heavily improved the performance of general supervised models, time series, speech recognition, object detection and classification, and sentiment analysis.

Factorization machines are a relatively new and powerful tool for modeling high-dimensional and sparse data. Most commonly they are used as recommender systems by modeling the relationship between users and items. For example, factorization machines can be used to recommend your next Netflix binge based on how you and other streamers rate content.

In this session, participants will use recurrent neural networks to analyze sequential data and improve the forecast performance of time series data, and use convolutional neural networks for image classification. Participants will also use an intelligent search method to efficiently tune the hyperparameters of both deep learning models…more details

Training | Open Source | Beginner-Intermediate

Data scientists are using graphs to help improve predictions and enhance their machine learning models. Application developers are extending their use of Neo4j’s native graph platform to incorporate native graph algorithms to build more intelligent applications. Using the two together shows us how connected data can impact our understanding of data structure and improve our analysis and predictions. Graph data analytics provides capabilities and insight that other tools and stores cannot uncover.
In this session, you will learn how to combine Neo4j, the Cypher query language, and graph algorithms for data science and analytics uses. The presenters will discuss the Neo4j graph database through an analytics lens and explain how Neo4j’s graph algorithms library is architected…more details

Workshop | Machine Learning | Intermediate

Building machine learning models that are accurate and bring value to your organization is not an easy task. You need to define the problem, find and clean the data, engineer features, select and tune machine learning models, evaluate it and put it in production. It’s is quite a lot already, but today we are starting to care more and more about explainability and fairness of machine learning models and the impact it has on the society at large. This adds yet another level of complexity. How should you approach it and what are the tools that can help you do that?..more details

Training | Machine Learning | Open Source | All Levels

Modern statistics has become almost synonymous with machine learning, a collection of techniques that utilize today’s incredible computing power. This two-part course focuses on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theory behind the curtain. We start with the foundation of it all, the linear model and its generalization, the glm. We look how to assess model quality with traditional measures and cross-validation and visualize models with coefficient plots. Next we turn to penalized regression with the Elastic Net. After that we turn to Boosted Decision Trees utilizing xgboost. Attendees should have a good understanding of linear models and classification and should have R and RStudio installed, along with the `glmnet`, `xgboost`, `boot`, `ggplot2`, `UsingR` and `coefplot` packages…more details

Training | Data Visualization | All levels

As data scientists, we are expected to be experts in machine learning, programming, and statistics. However, our audiences might not be! Whether we’re working with peers in the office, trying to convince our bosses to take some sort of action, or communicating results to clients, there’s nothing more clear or compelling than an effective visual to make our point. Let’s leverage the Python libraries Matplotlib and Bokeh along with visual design principles to make our point as clearly and as compellingly as possible!…more details

Workshops | Machine Learning | Open Source | Intermediate-Advanced

Document Clustering is a powerful application of Natural Language Processing techniques useful in topic modeling and extraction and information retrieval. A central challenge in document clustering is the scale of the data involved, making document clustering a prime candidate for development with the open source, distributed processing framework, Apache Spark. At the same time, tremendous strides have been made in the past few years in the development of other open source tools useful in NLP applications. This workshop will make heavy use of Apache Spark, but look at integrating these other tools (Gensim, Scikit-Learn) to the development process. Additionally, particular attention will be paid to NLP and document clustering as an application of Unsupervised Learning…more details

Tutorials | Data Visualization | Open Source | Beginner

Resampling methods like the bootstrap are becoming increasingly common in modern data science. For good reason too; the bootstrap is incredibly powerful. Unlike t-statistics, the bootstrap doesn’t depend on a normality assumption nor require any arcane formulas. You’re no longer limited to working with well understood metrics like means. One can easily build tools that compute confidence for an arbitrary metric. What’s the standard error of a Median? Who cares! I used the bootstrap.

With all of these benefits the bootstrap begins to look a little magical. That’s dangerous. To understand your tool you need to understand how it fails, how to spot the failure, and what to do when it does. As it turns out, methods like the bootstrap and the t-test struggle with very similar types of data. We’ll explore how these two methods compare on troublesome data sets and discuss when to use one over the other…more details

Tutorials | Data Science for Good | Machine Learning

Climate change is one of the greatest challenges facing humanity, and data scientists may wonder how we can help. In this talk, we will see how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we explore high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. These problems lead to exciting research questions as well as promising business opportunities…more details

Workshop | Machine Learning | Data Visualization | Intermediate

Most implementations of supervised machine learning algorithms are designed to work with complete datasets, but datasets are rarely complete. This dichotomy is usually addressed by either deleting points with missing elements and losing potentially valuable information or imputing (trying to guess the values of the missing elements), which can lead to increased bias and false conclusions. I will quickly review the three types of missing data (missing completely at random, missing at random, missing not at random) and a couple of simple but often misleading ways to impute. I will spend most of the time describing three advanced methods for handling missing data: multiple imputation, the reduced-feature (aka pattern submodel) approach, and XGBoost, which is one of the few machine learning algorithms that works with incomplete datasets. We will discuss the advantages and limitations of each of these methods…more details

Talk | Research Frontiers | Deep Learning  | Beginner-Intermediate

Reinforcement Learning (RL) has been applied to diverse problem domains with varied success. Applications to game domains have surpassed all expectations and achieved super-human performance, while applications to robotics and other areas involving control of physical systems have been more limited. This tutorial will discuss model-based methods that can substantially increase performance, both in terms of computational efficiency and quality of solutions. While RL relies on simulation models to sample data, models can do much more than generate samples. From a software perspective one can think of a physics simulator as a library providing many API function calls, only one of which is used to generate samples. Why not leverage the rest of the underlying functionality to develop better learning algorithms? This involves combining ideas from machine learning with ideas from geometry, physics, control theory and numerical optimization that go beyond learning from data. The tutorial will include examples from recent research projects, as well as demonstration of (not yet released) software called Optico – which is a unified framework for model-based optimization developed on top of the MuJoCo physics simulator…more details