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Week 1 Summary

1. Machine Learning

Field of study that gives computers the ability to learn without being explicitly programmed.

2. AI General Intelligence

  • Building machines as intellgent as humans.
  • Machines try to behave, mimic human brain.

3. Supervised Machine Learning

  • Referes to Alogrithms that learn input to output mapping, x -> y mapping
  • You provide learning algorithms expamples to learn from lables (right answers).
  • Call it Supervised, baceause we try to supervise algorithm to give answers for a given input.

i) Regression

  • Type of supervised learning in which a learning algorithm learns to predict a number out of infinte possible values.

ii) Classification

  • Type of supervised learning in which we have to predict a category from a fixed set of possible values.
  • Classification algorithm predict categories. Categories can be numeric or non-numeric.

4. Unseprvised Machine Learning

  • Given data isn't associated with any output lables.
  • We are not asked to diagnose whether a Tumor is Benign or Malignant. Instead, we have to find some pattern, structure, or something interesting in the data.
  • We are not asked to predict output label.
  • Call it unsupervised, baceause we are not trying to supervise algorithm to give right answers for a given input. Instead, we figure out what is interesting in the data.

i) Clustering

  • Find groups, clusters in the data.

  • We divide (un-labelled) data in different clusters based on similar characteristics.

  • Group similar data points together.

  • Example <1> group customers into different market segments.

    <2> categories of learners, e.g., growing skills, develop career, stay updated with AI.

ii) Anomaly Detection

  • Find something unsual in the data.
  • Find unsual data points, e.g., unusual transactions.

iii) Dimentionality Reduction

  • Compress data using fewer numbers.
  • Compress a large dataset into smaller dataset while loosing as little information as possible.
  • Usually, resultant dataset has reduced features.

5. Types of Supervised Learning

  • Regression
  • Classification

6. Types of Unsuprvised Learning

  • Clustering
  • Anomaly Detection
  • Dimentionality Reduction

7. Regression vs Classification Model

  • Regression model > predicts numbers out of infinit possible values
  • Classification models > predicts categories out of discrete, finite set of ouputs.

Linear Regression Model

  • Model fits straight line to data.

i) How do you get a trained Model?

Training Set > Learning Algo > Model f(x)

ii) How Model predicts output?

Features (x) > Model f(x) > Prediction (y^)

iii) How to represent f(x)?

$$ f_{w,b}(x^{(i)}) = wx^{(i)} + b \tag{1}$$

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