Introduction to Machine Learning

• What is Machine Learning (ML)
• Machine Learning is study of computer algorithms that improve experience
• Seen as subset of Artificial Intelligence (AI)
• ML algorithms build a mathematical model based on sampe data in order to make predictions or decisions without being explicitly programmed
• Also referred to as Predictive Analytics in its applications business problems.
  
• Where is ML Used?
• Used in wide variety of applications
  • Email Filtering
  • Loan Prediction
  • Sales Forecasting
  • Movie Recommendation
• Why Machine Learning?
• Programs to solve problems like facial recognition are very hard
• Instead of writing a program, collect lots of examples that specify the correct output for a given input
• An ML algorithm then takes these examples and produces a program that does the job
  • Program works for new cases & ones it trained on
• Requirements of ML
• Some pattern must exist between the input and output of data (the relationship cannot be completely random)
  • Lottery winning numbers cannot be predicted using ML
• Enough data that needs to be present to discover this pattern
• No easily definable set of rules or mathematical formulas should exist that can explain the relationship between input and output of the data
  • If it did, we can use traditional programming
• Examples of TASKS BEST SOLVED WITH ML
• Recognizing patterns
  • Facial identities
  • Handwritten or spoken words
• Recognizing Anamolies
  • Unusual sequence of credit card transactions
  • Unusual patterns of sensor readings in nuclear power plant
  • Unusual sound in the car engine
• Prediction:
  • Future Stock prices or currency exchange rates
  • Employee attrition
  • Loan defaulting
• More Examples:
  • Spam filtering, Fraud detection
  • Recommendation Systems
  • Autonomous cars
  • Speech recognition
  • Image classification/tagging
  • Language translation
  • Price Prediction
  • Advertising: Ad click prediction
  • Playing games (Chess, Go, StarCraft etc.)
• Machine Learning Approaches:
• Traditionally divided into three categories
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning