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Overview of Data Science and AI

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johnstamford

The document provides an overview of data science, artificial intelligence, and machine learning. It discusses the differences between AI and machine learning, as well as what constitutes data science. Examples are given of applying data science in healthcare to study the impact of remote patient monitoring devices and identify high-risk patients. State-of-the-art machine learning techniques like neural networks, deep learning, and deep reinforcement learning are also overviewed. Finally, the document discusses how companies are using data science and AI and provides next steps for learning and applying these fields.

Data & Analytics
1 of 25
Overview of Data science and AI JOHN STAMFORD
Aims  Aim 1 - Overview of Data science and AI  Artificial Intelligence / Machine Learning / Data Science  Aim 2 - Example of Data Science in health care  Aim 3 - Example of ‘state of the art’ machine learning  Aim 4 - How companies are using data science and AI
Who?  Director of Machine Learning @ Spencers Group  Lecturer in Intelligent Systems and Robotics @ De Montfort University  Teach Statistical Programming in R  C4Di Member  6 years experience of working in Machine Learning Projects  PhD in Computer Science/Machine Learning/Data Science (submit soon)  Clinical predictive modelling for patients with Chronic Heart Failure  Post-Graduate Certificate in Research  Statistical Modeling and Statistical Programming in R  MSc Intelligent Systems and Robotics
What is the difference between AI and Machine Learning AI  The perception  The presentation  The bits you see/interactive with  Term for Media / C-Suite Machine Learning  The technical aspects  The ‘doing’ bits  The Maths!!!  Term for recruitment / technical teams
What about Data Science
Who is In Demand Source: https://www.indeed.com/jobtrends/
Who is In Demand Data Scientist: The Sexiest Job of the 21st Century Harvard Business Review (2012) 13 Top Tech Skills In High Demand For 2018 Forbes (2017) 1. Experience With AI 3. Data Science Talent 9. Applied Machine Learning 11. Any Skills Related To Analytics UK demand for AI professionals has almost tripled in three years Computer Weekly (2018) Million-dollar babies The Economist (2016)
Data Scientist / Machine Learning Engineer Skillset  Experience/Qualifications  MSc (in machine learning, AI or CS) with experience (+2 years)  PhD (in machine learning, AI or CS)  Languages/Technical  Python, R, Weka, Matlab  Scikit-learn, numpy, scipy, NLTK, Stanford CoreNLP, TensorFlow  C++, Java  ML Specific  Solid understanding of machine learning fundamentals  Strong opinions on a wide range of statistical approaches e.g. Logistic Regression, Decision Trees, etc
Data Science in action HEALTH CARE EXAMPLE
Data Science in action (health care)  ‘what impact will <something> have on <something else>?  Real world example…  Philips Healthcare make a device call Motiva  It allows remote monitoring of patients  Question: what impact does it have?  Challenge  What has been done before?  Who has used it?  Can be compare with other people?  How do we measure ‘impact’?
Data Science in action (health care)  Who?  150 people used it  Over 3,000 didn’t  How do we match and extract similar patients?  Skills  Data Extraction (SQL)  Data Visualisation  Statistics  Analytical  Predictive
Data Science in action (health care)
Data Science in action (health care)  So what have we actually done?  A/B Testing (with a difference)  Does one <pathway/method> give better results?  How can we apply it?  Marketing strategy  Product Development  See ‘The Lean Startup’ by Eric Ries  Difference?  Findings – Broader and based on scientific methods  Our methods account for ‘chance’
Now we know it works can we Identify High Risk Patients (EARLY WORK, MORE TO COME AFTER PHD SUBMISSION)  Predict high risk patients using  Statistical Learning  Machine Learning  Results  How do you measure best?  Sensitivity vs Specificity  Impact of the decision  Caused an interesting debate
It is all about The story  So now we know  HTM works in Hull and East Riding for patients with Heart Failure  We can identify patients who are at greatest risk of death or readmission  What we don’t know  Why?  Which elements?  Other outcomes  Peer reviewed papers  In-depth look at the algorithms used (new insights into their usage)
Examples of Machine Learning NEURAL NETWORK (ONLINE) DEEP LEARNING (IMAGE CLASSIFICATION) DEEP REINFORCEMENT LEARNING (SELF LEARNING AGENTS)
Example Neural Network  Loosely based on real neurons  Mathematical models DEMO: https://playground.tensorflow.org/
Example DEEP LEARNING  Neural Networks +  More Layers  Combination of different types of layers  Example – Convolutional Neural Networks  Image classification  Object Detection  Loosely based on cat’s visual cortex [1] [1] Deep Learning Tutorial - http://deeplearning.net/tutorial/deeplearning.pdf Stamford. J, and Peach. B, (2016) “Scene Detection using Convolutional Neural Networks”, 2nd IET International Conference on Technologies for Active and Assisted Living Another Example (Python and TensorFlow): https://www.youtube.com/watch?v=_zZe27JYi8Y https://www.youtube.com/watch?v=mtu9_w8B984
Example DEEP LEARNING Stamford. J, and Peach. B, (2016) “Scene Detection using Convolutional Neural Networks”, 2nd IET International Conference on Technologies for Active and Assisted Living
Example Deep Reinforcement Learning  Q-Learning  Q(s,a)  Learns – what is the best action (a) based on the current state (s)  States – convolution process  MSc Project - Playing Atari 2600 Games with Deep RL  Microsoft  Deepmind  Google acquires (£400 million)  Examples:  Deep Q Learning playing Atari (Link)  Unity (Github, ML Examples, Demo)
How are companies using AI/ML/DS?
How are companies using AI/Machine Learning/Data Science
Next Steps  LEARN  Statistics (if DS)  Python  SciKit Learn  Kaggle  Udemy  DMU MSc Intelligent Systems  £6k (Link)  Distance Learning  Apply/Demo your knowledge  HIRE  Experience  Qualifications  Publications  Challenges  High demand / Low supply  How do you assess a candidate?  Expectations  R&D Tax Credits  SME: 230% (Link)
Recap  Aim 1 - Overview of Data science and AI  Artificial Intelligence / Machine Learning / Data Science  Aim 2 - Example of Data Science in health care  Aim 3 - Example of ‘state of the art’ machine learning  Aim 4 - How companies are using data science and AI
Thank you @johnstamford linkedin.com/in/johnstamford/

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Overview of Data Science and AI

  • 1. Overview of Data science and AI JOHN STAMFORD
  • 2. Aims  Aim 1 - Overview of Data science and AI  Artificial Intelligence / Machine Learning / Data Science  Aim 2 - Example of Data Science in health care  Aim 3 - Example of ‘state of the art’ machine learning  Aim 4 - How companies are using data science and AI
  • 3. Who?  Director of Machine Learning @ Spencers Group  Lecturer in Intelligent Systems and Robotics @ De Montfort University  Teach Statistical Programming in R  C4Di Member  6 years experience of working in Machine Learning Projects  PhD in Computer Science/Machine Learning/Data Science (submit soon)  Clinical predictive modelling for patients with Chronic Heart Failure  Post-Graduate Certificate in Research  Statistical Modeling and Statistical Programming in R  MSc Intelligent Systems and Robotics
  • 4. What is the difference between AI and Machine Learning AI  The perception  The presentation  The bits you see/interactive with  Term for Media / C-Suite Machine Learning  The technical aspects  The ‘doing’ bits  The Maths!!!  Term for recruitment / technical teams
  • 6. Who is In Demand Source: https://www.indeed.com/jobtrends/
  • 7. Who is In Demand Data Scientist: The Sexiest Job of the 21st Century Harvard Business Review (2012) 13 Top Tech Skills In High Demand For 2018 Forbes (2017) 1. Experience With AI 3. Data Science Talent 9. Applied Machine Learning 11. Any Skills Related To Analytics UK demand for AI professionals has almost tripled in three years Computer Weekly (2018) Million-dollar babies The Economist (2016)
  • 8. Data Scientist / Machine Learning Engineer Skillset  Experience/Qualifications  MSc (in machine learning, AI or CS) with experience (+2 years)  PhD (in machine learning, AI or CS)  Languages/Technical  Python, R, Weka, Matlab  Scikit-learn, numpy, scipy, NLTK, Stanford CoreNLP, TensorFlow  C++, Java  ML Specific  Solid understanding of machine learning fundamentals  Strong opinions on a wide range of statistical approaches e.g. Logistic Regression, Decision Trees, etc
  • 9. Data Science in action HEALTH CARE EXAMPLE
  • 10. Data Science in action (health care)  ‘what impact will <something> have on <something else>?  Real world example…  Philips Healthcare make a device call Motiva  It allows remote monitoring of patients  Question: what impact does it have?  Challenge  What has been done before?  Who has used it?  Can be compare with other people?  How do we measure ‘impact’?
  • 11. Data Science in action (health care)  Who?  150 people used it  Over 3,000 didn’t  How do we match and extract similar patients?  Skills  Data Extraction (SQL)  Data Visualisation  Statistics  Analytical  Predictive
  • 12. Data Science in action (health care)
  • 13. Data Science in action (health care)  So what have we actually done?  A/B Testing (with a difference)  Does one <pathway/method> give better results?  How can we apply it?  Marketing strategy  Product Development  See ‘The Lean Startup’ by Eric Ries  Difference?  Findings – Broader and based on scientific methods  Our methods account for ‘chance’
  • 14. Now we know it works can we Identify High Risk Patients (EARLY WORK, MORE TO COME AFTER PHD SUBMISSION)  Predict high risk patients using  Statistical Learning  Machine Learning  Results  How do you measure best?  Sensitivity vs Specificity  Impact of the decision  Caused an interesting debate
  • 15. It is all about The story  So now we know  HTM works in Hull and East Riding for patients with Heart Failure  We can identify patients who are at greatest risk of death or readmission  What we don’t know  Why?  Which elements?  Other outcomes  Peer reviewed papers  In-depth look at the algorithms used (new insights into their usage)
  • 16. Examples of Machine Learning NEURAL NETWORK (ONLINE) DEEP LEARNING (IMAGE CLASSIFICATION) DEEP REINFORCEMENT LEARNING (SELF LEARNING AGENTS)
  • 17. Example Neural Network  Loosely based on real neurons  Mathematical models DEMO: https://playground.tensorflow.org/
  • 18. Example DEEP LEARNING  Neural Networks +  More Layers  Combination of different types of layers  Example – Convolutional Neural Networks  Image classification  Object Detection  Loosely based on cat’s visual cortex [1] [1] Deep Learning Tutorial - http://deeplearning.net/tutorial/deeplearning.pdf Stamford. J, and Peach. B, (2016) “Scene Detection using Convolutional Neural Networks”, 2nd IET International Conference on Technologies for Active and Assisted Living Another Example (Python and TensorFlow): https://www.youtube.com/watch?v=_zZe27JYi8Y https://www.youtube.com/watch?v=mtu9_w8B984
  • 19. Example DEEP LEARNING Stamford. J, and Peach. B, (2016) “Scene Detection using Convolutional Neural Networks”, 2nd IET International Conference on Technologies for Active and Assisted Living
  • 20. Example Deep Reinforcement Learning  Q-Learning  Q(s,a)  Learns – what is the best action (a) based on the current state (s)  States – convolution process  MSc Project - Playing Atari 2600 Games with Deep RL  Microsoft  Deepmind  Google acquires (£400 million)  Examples:  Deep Q Learning playing Atari (Link)  Unity (Github, ML Examples, Demo)
  • 21. How are companies using AI/ML/DS?
  • 22. How are companies using AI/Machine Learning/Data Science
  • 23. Next Steps  LEARN  Statistics (if DS)  Python  SciKit Learn  Kaggle  Udemy  DMU MSc Intelligent Systems  £6k (Link)  Distance Learning  Apply/Demo your knowledge  HIRE  Experience  Qualifications  Publications  Challenges  High demand / Low supply  How do you assess a candidate?  Expectations  R&D Tax Credits  SME: 230% (Link)
  • 24. Recap  Aim 1 - Overview of Data science and AI  Artificial Intelligence / Machine Learning / Data Science  Aim 2 - Example of Data Science in health care  Aim 3 - Example of ‘state of the art’ machine learning  Aim 4 - How companies are using data science and AI

Editor's Notes

  1. In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate).