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NUS Institute of Systems Science

NICF- Text Processing Using Machine Learning (SF)

Available dates

Dec 2—6, 2019
5 days
Singapore
SGD 4815 ≈USD 3537
SGD 963 per day

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About the course

We are in an era where AI and analytics are transforming industries and people’s life at an unprecedented pace. In the recently released report from Gartner, Top 10 Strategic Technology Trends for 2018, the top two trends are AI Foundation, and Intelligent Apps and Analytics.

AI Foundation focuses on creating systems that learn, adapt and potentially act autonomously, and leveraging AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience. The technologies and techniques in AI Foundation have grown substantially over the years, as the availability of massive amounts of data has fed machine learning, resulting in the flourishing of more advanced algorithms in the form of deep learning.

The second trend, Intelligent Apps and Analytics, clearly states AI’s huge impact in the next-generation data and analytics paradigm, Augmented Analytics. Machine learning is key in this new paradigm, automating data preparation, insight discovery and insight sharing for a broad range of end-users and citizen data scientists, while expert data scientists focusing on specialized problems and on embedding models into applications. The need to perform processing on natural language data is reflected in the illustrated paradigm, identifying three tasks in this area – natural language processing (NLP), natural language query (NLQ), natural language generation (NLG).

In the field of NLP, deep learning techniques has taken a dominant position over tradition statistical methods. Researchers have been reporting much higher performance metrics applying deep learning to solve problems like text classification, language modeling, speech recognition, caption generation, machine translation, document summarization, question answering, etc.

This course, Text Processing Using Machine Learning, provides essential knowledge and skills required to perform deep learning based text processing in common tasks encountered in industries. A combination of lectures, case studies, and workshops will be used to cover the application of DL techniques such as word-embedding, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, character-based language modelling, encoder-decoder models, reinforcement learning, etc.

This course is part of the Stackable Certificate Programme in Business Analytics offered by NUS-ISS.

Key Takeaways

At the end of the course, the participants will be able to:

  • Identify common tasks that industry has with textual data
  • Gain a practical understanding about advanced machine learning techniques for NLP
  • Acquire proficiency in implementing and creating NLP models for the above tasks
  • Learn how the fundamentals and cutting-edge machine learning approaches work together for performing text-related tasks in industry.

What Will Be Covered

This 5 day course course provides essential knowledge and skills required to perform deep learning based text processing in common tasks encountered in industries.

This course will cover:

  • NLP and Deep Learning
  • Deep Learning Foundations
  • Word Embeddings
  • Text Classification
  • Language Models and Recurrent Neural Networks
  • Encoder-Decoder Models
  • Memory Networks
  • NLP & Bayesian Methods
  • Parsing

Who should attend

This course is designed for professional who would like to learn skills to implement advanced machine learning techniques such as deep learning techniques in building NLP models for performing common text processing tasks in industry. It will be useful for:

  • Machine learning engineers
  • Data scientists
  • Data analysts

Pre-requisites The course expects the participants to have strong programming skills (using python) and basic background knowledge of machine learning and model building.

Trust the experts

Fan Zhen Zhen

Zhenzhen has been with Institute of Systems Science, NUS, since 2006. She currently lectures in the Master of Technology programme in the areas of case-based reasoning, text mining, KBS development, hybrid KBS, and formal specification. Prior to joining ISS, she was a senior research engineer at ...

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