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Intro to machine learning

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Govind Mudumbai
Govind Mudumbai

An introduction to Machine Learning

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Intro to Artificial Intelligence by Govind Mudumbai
Index  Why this Topic?  What is Machine Learning?  Types of ML and their applications  Why now?  When and how to use? Process  Applications at Empirix
Why this topic  Machine Learning – subfield of AI  Deep Learning – Neural Networks  Reinforcement Learning  Blockchain – Secure, Immutable, Distributed Ledger  Gene Cell Therapy & CRISPR – Gene Editing  Quantum Computing – Super fast. Qubits Feynman Technique
What is Machine Learning?  Algorithm to predict with probability by recognizing a pattern  Think of it like a baby/kid learning stuff as they grow  Process:  Predict – Hypothesis & Parameters  Run Error function – Cost Function  Learn – Minimize Cost Function
Types of ML Unsupervised Learning Reinforcement Learning Supervised Learning
Unsupervised Learning  Data is not labeled  Take data and puts in a bin according to some its properties  Segments data by population  Clustering  Examples:  Google News articles  Group people according to Genes/Genome  Astronomical Data Analysis  Market Segmentation
Reinforcement Learning  Kind of true AI?!  Semi-supervised learning  Not fully labeled dataset  Rewards/Goal based learning  Algorithm figures out on its own the rules & best strategy to achieve goal  Deep Reinforcement Learning – Deep Q Networks  Pretty advanced – very promising  Examples:  Self driving Car  AlphaGo, Poker, Chess, Super-Mario, Atari and other games  Natural Language Processing  Robots taking a step
Supervised Learning  Training data is labeled  Algorithm is trained  Linear Regression – output a number  What temperature tomorrow?  Price of house  How many units will sell  Logistic Regression  Binary Classification: spam or not, cancer or not, will customer buy or not etc.  Multiclass Classification: genre of movie, product category in Craigslist etc.  Deep Learning/Neural Networks  Emulate the way our brain works  Multiple layers of neurons: thus ‘deep’
Applications of Supervised Learning in Industry  Speech Recognition  Natural Language Processing – Siri & Google Assistant  Image Processing  Recognizing Faces, OCR etc.  Health diagnostics – Radiology  Chatbot  Judicial Decisions – circumvent human bias  Online Advertisement  Spam filtering  Sentiment Analysis  And many many more……
Why now? High Performance Computing – HPC Big Data
HPC  Matrix-multiplication operations – need lot of resources  GPU based computing - NVidia  Cloud Computing – AWS, Azure, Google Cloud etc.  GPU-based VM Instances for running training algorithms  Services provided via API
Big Data  Amount of data generated recently  Large companies like Facebook, Google, Amazon, Baidu  China
When to Use Machine Learning  You cannot code the rules: Many human tasks (such as recognizing whether an email is spam or not spam) cannot be adequately solved using a simple (deterministic), rule-based solution. A large number of factors could influence the answer. When rules depend on too many factors and many of these rules overlap or need to be tuned very finely, it soon becomes difficult for a human to accurately code the rules. You can use ML to effectively solve this problem.  You cannot scale: You might be able to manually recognize a few hundred emails and decide whether they are spam or not. However, this task becomes tedious for millions of emails. ML solutions are effective at handling large-scale problems. http://docs.aws.amazon.com/machine-learning/latest/dg/when-to-use-machine-learning.html
Data Science Process  Define the problem  Collect data for the problem  Clean data  Data Wrangling  Dividing data into training and test sets  Feature Engineering  Run ML Algorithms  Use resulting features  Generate Predictions  Visualize Results – Graphs, Charts  Present Results
Math  Linear Algebra  Statistics & Probability  Calculus  Languages Used: Python, R, Matlab/Octave  ML Frameworks: TensorFlow, Theano, Keras, PyTorch  Visualization: D3js, Tableau  Data Wrangling/Storage: All big data techs  Apache Spark, Redis  Apache Hadoop – HDFS, Hive, Pig, HBase Technical Trends
At Empirix  Speech Recognition – instead of Nuance SR  Improve Sales/Marketing – using data in Salesforce  A support chatbot  Pattern recognition from all the customer data – New opportunities Tell me more…

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Intro to machine learning

  • 2. Index  Why this Topic?  What is Machine Learning?  Types of ML and their applications  Why now?  When and how to use? Process  Applications at Empirix
  • 3. Why this topic  Machine Learning – subfield of AI  Deep Learning – Neural Networks  Reinforcement Learning  Blockchain – Secure, Immutable, Distributed Ledger  Gene Cell Therapy & CRISPR – Gene Editing  Quantum Computing – Super fast. Qubits Feynman Technique
  • 4. What is Machine Learning?  Algorithm to predict with probability by recognizing a pattern  Think of it like a baby/kid learning stuff as they grow  Process:  Predict – Hypothesis & Parameters  Run Error function – Cost Function  Learn – Minimize Cost Function
  • 5. Types of ML Unsupervised Learning Reinforcement Learning Supervised Learning
  • 6. Unsupervised Learning  Data is not labeled  Take data and puts in a bin according to some its properties  Segments data by population  Clustering  Examples:  Google News articles  Group people according to Genes/Genome  Astronomical Data Analysis  Market Segmentation
  • 7. Reinforcement Learning  Kind of true AI?!  Semi-supervised learning  Not fully labeled dataset  Rewards/Goal based learning  Algorithm figures out on its own the rules & best strategy to achieve goal  Deep Reinforcement Learning – Deep Q Networks  Pretty advanced – very promising  Examples:  Self driving Car  AlphaGo, Poker, Chess, Super-Mario, Atari and other games  Natural Language Processing  Robots taking a step
  • 8. Supervised Learning  Training data is labeled  Algorithm is trained  Linear Regression – output a number  What temperature tomorrow?  Price of house  How many units will sell  Logistic Regression  Binary Classification: spam or not, cancer or not, will customer buy or not etc.  Multiclass Classification: genre of movie, product category in Craigslist etc.  Deep Learning/Neural Networks  Emulate the way our brain works  Multiple layers of neurons: thus ‘deep’
  • 9. Applications of Supervised Learning in Industry  Speech Recognition  Natural Language Processing – Siri & Google Assistant  Image Processing  Recognizing Faces, OCR etc.  Health diagnostics – Radiology  Chatbot  Judicial Decisions – circumvent human bias  Online Advertisement  Spam filtering  Sentiment Analysis  And many many more……
  • 10. Why now? High Performance Computing – HPC Big Data
  • 11. HPC  Matrix-multiplication operations – need lot of resources  GPU based computing - NVidia  Cloud Computing – AWS, Azure, Google Cloud etc.  GPU-based VM Instances for running training algorithms  Services provided via API
  • 12. Big Data  Amount of data generated recently  Large companies like Facebook, Google, Amazon, Baidu  China
  • 13. When to Use Machine Learning  You cannot code the rules: Many human tasks (such as recognizing whether an email is spam or not spam) cannot be adequately solved using a simple (deterministic), rule-based solution. A large number of factors could influence the answer. When rules depend on too many factors and many of these rules overlap or need to be tuned very finely, it soon becomes difficult for a human to accurately code the rules. You can use ML to effectively solve this problem.  You cannot scale: You might be able to manually recognize a few hundred emails and decide whether they are spam or not. However, this task becomes tedious for millions of emails. ML solutions are effective at handling large-scale problems. http://docs.aws.amazon.com/machine-learning/latest/dg/when-to-use-machine-learning.html
  • 14. Data Science Process  Define the problem  Collect data for the problem  Clean data  Data Wrangling  Dividing data into training and test sets  Feature Engineering  Run ML Algorithms  Use resulting features  Generate Predictions  Visualize Results – Graphs, Charts  Present Results
  • 15. Math  Linear Algebra  Statistics & Probability  Calculus  Languages Used: Python, R, Matlab/Octave  ML Frameworks: TensorFlow, Theano, Keras, PyTorch  Visualization: D3js, Tableau  Data Wrangling/Storage: All big data techs  Apache Spark, Redis  Apache Hadoop – HDFS, Hive, Pig, HBase Technical Trends
  • 16. At Empirix  Speech Recognition – instead of Nuance SR  Improve Sales/Marketing – using data in Salesforce  A support chatbot  Pattern recognition from all the customer data – New opportunities Tell me more…