Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit’s work involves applying machine learning techniques to complex problems in the IoT and Telecoms domains. You can follow him on twitter @AjitJaokar and his blogs at Future Text.
We are beyond thrilled to announce that Ajit will not only be speaking at our Big Data, Berlin meetup February 17, but he will also be at the head of the second workshop of our ‘Dataconomy Presents’ series. In the same vein as our 3-day intensive IoT workshop with thought leader Alexandra Deschamps-Sonsino, Ajit’s will be a four-part course that aims at laying the foundation for those who want to steer their professional path in the right direction.
The Artificial Intelligence for the Enterprise workshop targets developers and architects who want to transition their career to Enterprise AI. The course correlates the new AI ideas with familiar concepts like ERP, data warehousing etc. and helps to make the transition easier. The course is based on a logical concept called an ‘Enterprise AI layer’. This AI layer is focused on solving domain specific problems for an Enterprise. We could see such a layer as an extension to the Data Warehouse or the ERP system (an Intelligent Data Warehouse/ Cognitive ERP system). Thus, the approach provides tangible and practical benefits for the Enterprise with a clear business model.
The course consists of four parts. The first one will take place in March 2017, and will touch on the following topics-
I. March 2017
- Understanding the Enterprise AI layer
- Introduction to Machine Learning
- Unsupervised Learning
- Supervised Learning
- Generalized Linear Modeling
- Gradient Boosting Machine
- Ensembles
- Random Forest
- Programming foundations
The next three parts will take place from April through August, divided into two more workshops and a ‘Projects and Deployment’ section.
II. April 2017
- Introduction to Deep Learning
- Multilayer Perception
- Auto encoders
- Deep Convolutional Networks
- Recurrent Neural Networks
- Reinforcement learning
- Programming foundations
III. May/June 2017
- Natural language processing
- Basics of Text Analytics
- POS Tagging
- Sentiment Analysis
- Text Classification
- Intelligent bots
- Programming foundations
IV. June/August 2017 – Projects and deployment
Deploying Enterprise AI
- Acquiring Data and Training the Algorithm
- Processing and hardware considerations
- Business Models – High Performance Computing – Scaling and AI system
- Costing an AI system
- Creating a competitive advantage from AI
- Industry Barriers for AI
Implementation of Enterprise AI use cases (in groups)
- Healthcare
- Insurance
- Adtech
- Fraud detection
- Anomaly detection
- Churn, classification
- Customer analytics
- Natural Language Processing, Bots and Virtual Assistants
Keep in mind:
- The course covers Design of Enterprise AI, Technology foundations of Enterprise AI systems, Specific AI use cases, Development of AI services and Deployment and Business models
- The implementation / development for the course is done using R, Python and Spark using the H2O APIs
- For Deep learning, we will work with GPUs, tensoflow, Mxnet and Caffe
- We focus on large scale problems
- Notes on Programming foundations: We assume that you have significant Programming knowledge. However, we do not assume that you are familiar with Python, R or Spark.
- The syllabus is subject to change
The course will give you a background in these languages over the first three months. You will then use this knowledge to work on the use cases in the Project phase. H2O.ai will be in charge of validating the group projects. Certification of completion of the whole course is based on completing a quiz related to modules worked on during the course.
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Image: Thomas Hawk, CC 2.0
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