Workshop: [SOLD OUT] Machine Learning & AI: Core Techniques Workshop
With Python, TensorFlow and a plethora of other open source tools, anyone with a computer can run machine learning algorithms in a jiffy! However, without an understanding of which algorithms to choose and when to apply a technique, most machine learning efforts turn into trial and error experiments with conclusions like “The algorithms don’t work” or “Perhaps we should get more data”.
In this workshop, we will focus on the key tenets of machine learning algorithms and how to choose an algorithm for a problem. Rather than just showing how to run experiments in Python or TensorFlow, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.
What you’ll Learn:
- Machine Learning: An intuitive foundation
- The Machine Learning pipeline
- Supervised Learning: Classification and Prediction
- Machine learning methods: Regression, KNN, Random Forests, Neural Networks
- Evaluating performance
- Case study 1: Predicting interest rates in Freddie Mac mortgage data
- Case study 2: To give a loan or not using Lending club data
Other Workshops:
Tracks
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Blameless Culture
Absorb the lessons learned from failures and outages in a human-centric process.
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Modern Computer Science in the Real World
Learn how companies are applying recent CS research to tackle concurrency, distributed data, and coordination.
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Architectures You’ve Always Wondered About
Join companies like Google, Netflix, Bloomberg, BBC, and more as they share an inside glimpse on their next-gen architectures and challenges of delivering at massive scale.
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Bare Knuckle Performance
Learn from practitioners on the challenges and benefits of architecting for performance and much more.
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Java - The Interesting Bits
Learn the new features in the recent and near-future releases of Java and the JVM and what they offer.
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Ethical Considerations in Consciously Designed Software
Design considerations for various contexts, locations, security and privacy requirements.
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Operating Microservices
Learn from practitioners operating and evolving systems in performance demanding environments.
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Shift-Left Cybersecurity: Developer Accountability for Security
Learn how to make security an inherent part of the software development process.
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Native Compilation Is Back (A Look at Non-Vm Compilation Targets)
Issues with native compilation for in browser-based and server-side environments
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Troubleshooting in Production
Learn debugging strategies for complex and high stakes environments where standard debuggers and profilers fail.
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Predictive Architectures and ML
Learn about cutting-edge ML applications and their underlying architectures.
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Mission Critical Data Engineering
Explore a variety of data engineering use-cases and applications
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Production Readiness
Observability, emergency response, capacity planning, release processes, and SLOs for availability and latency.
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Humane Leadership
A look at leadership with an emphasis on empathy, taking chances and building other leaders within organizations and teams
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Developer Experience: The Art and Science of Reducing Friction
Explore how to reduce developer friction between teams and stakeholders.