Introduction

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1. Introduction

What is Machine learning?

  • That gives computers the ability to lean without being explicitly programmed
  • You don’t have a full control of program anymore – as it will produce diff outcome

When to use machine learning?

  • Personalization- what is it that YOU are looking for?
  • Field of language- translation, Speech to text and text to speech
  • Online shopping – suggesting things you like, Netflix (confirms the prediction, if its wrong, the system learns)
    • Contest to improve the algorithm by 10%
  • Self-driving – distinguishing pedestrians from the road

AI

  • Supervised learning is mostly used to interpret larger amount of data
  • Reinforcement learning – has to do with control. Once you have the understanding of the world around you behaviour will change. It won’t lead you to same conclusion. (finds max reward)
  • When to write a normal program and when to write a ML program?
    • For normal programs, we have an algorithm, we implement it, and it outputs expected outcome.
    • ML becomes important when you don’t know what exactly you want, but you could provide examples
      • what music do you like? what exactly do you like about that genre?
      • Gather ‘features’, and use them to describe what you acc like

Supervised learning

  • Regression- learning the pattern of the function

    • Predictions from the past, and deriving predictions for the future
    • A graph that has a solid trend – would match with the model when new data points are given

  • Classification – shifting the burden of programming to throwing bunch of examples

    • e.g. distinguishing chair from a desk
    • Start by choosing features – stuff that differentiates the object
    • Then pass those features to algorithm classification

Unsupervised learning – we are still looking for categories, but the labels aren’t given

  • Clustering – these points belong together however, human beings have to go through them and evaluate
  • Reinforcement Learning – used to find the best possible path it should take in a specific situation. There is no answer, but the reinforcement agent decides what best to do to perform given test.

Machine learning process

  1. Data collection
    • you need some data to work with, are also responsible for gathering data. you need variety of data. E.g. face classification – you need other ethnic groups too
    • aware of the fact that all classes should be represented
  2. Feature selection
    • When you have bunch of raw data what are you gonna look at? – e.g. number of leg, surface, sizes etc
    • Deriving the distance btwn the ppl in the video – they recognize shaking hands, throwing stuff, etc depending on data, you need to be creative to see what it is that you r tryna look at
    • New way – deep learning! you really just give the neural network the image, and that will figure what’s important
  3. Algorithm choice
  4. Training
  5. Evaluation- tbat to tell how good the model is

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