introducing machine learning
TRANSCRIPT
MACHINE LEARNING
Machine can do what we do.
What is Machine Learning?A method of data analysis that automates analytical model building.
Using algorithms that iteratively learn from data.
In short, Machine Learning is make the machine learn something itself.
Applications of Machine Learning Face Detection used in Facebook and Coursera.
Language Understanding used in Slackbot.
Recommendations used in Netflix and Spotify.
Google and Baidu self driving car.
How it works?Typically classified into three boards categories:a. Supervised Learning
b. Unsupervised Learning
Supervised Learning “Right answer” given as final result - an input where the desired output is known.
Regression Problem - Predict continuous valued output
Supervised Learning “Right answer” given as final result - an input where the desired output is known.
Classification Problem
Supervised Learning Algorithms of supervised learning :
Logistic Regression
Linear Regression
Neural Network
ANOVA Quadratic Classifier
Decision Tree
Support Vector
Machine
Unsupervised Learning Used against data that has no historical labels. The algorithm must figure out what is being shown.
Clustering Problem
The teamAnswer the question, “Why are we the ones to solve the problem we identified?”
Market Segmentation
Social Network Analysis
News Summarisation
Recommender Systems
Unsupervised Learning Applications of unsupervised learning :
Unsupervised Learning Algorithms of unsupervised learning :
Hierarchical Clustering
K-mean Clustering
Gaussian Mixture Models
Hidden Markov Model
Quadratic Classifier
Decision Tree
Self-organizing Mapping
Recommender SystemsExample: Netflix Movie Recommendation
Alice (1) Brandon (2) Carmen (3) Dave (4)
Star War 1 1 5 4
Captain America
1 1 5 ?
Kung Fu Panda
? 4 1 ?
Zootopia 5 5 1 1
Lilo & Stitch 5 ? ? 1
nm = no. of
moviesnu = no. of users
r ( i , j ) = 1, if user j has rated movie iy ( i , j ) = rating given by user j to movie i
Recommender SystemsExample: Netflix Movie Recommendation
Alice (1) Brandon (2)
Carmen (3) Dave (4) Action (x1) Cartoon (x2)
Star War 1 1 5 4 1 0
Captain America
1 1 5 ? 1 0
Kung Fu Panda
? 4 1 ? 0 1
Zootopia 5 5 1 1 0 1
Lilo & Stitch 4 ? ? 1 0 1
Movie Features
Recommender SystemsProblem Formulation
r (i,j) = 1 if user j has rated movie iy (i,j) = rating given by user j to movie i
𝛳(j) = parameter vector for user jx(i) = feature vector for movie iFor user j and movie i, predicted rating = (𝛳(j))T(x(i))
To learn 𝛳(j):
Recommender SystemsProblem Formulation
To learn 𝛳(j) parameter for user j:
To learn 𝛳(1), 𝛳(1), … , 𝛳(nu):
Recommender SystemsCollaborative Filtering
Alice (1) Brandon (2)
Carmen (3) Dave (4) Action (x1) Cartoon (x2)
Star War 1 1 5 4 ? ?
Captain America
1 1 5 ? ? ?
Kung Fu Panda
? 4 1 ? ? ?
Zootopia 5 5 1 1 ? ?
Lilo & Stitch 4 ? ? 1 ? ?(𝛳(1))Tx(1) = 1 (𝛳(2))Tx(1)
= 1(𝛳(3))Tx(1) = 5 (𝛳(4))Tx(1)
= 4
Recommender SystemsProblem Formulation
Given 𝛳(1), 𝛳(2), … , 𝛳(nu), to learn x(i):
Given 𝛳(1), 𝛳(2), … , 𝛳(nu), to learn x(1), x(2), … x(nm), :
Recommender SystemsCollaborative Filtering
Given x(1), … x(nm) and movies rating, can estimate 𝛳(1), … , 𝛳(nu)Given 𝛳(1), … , 𝛳(nu), can estimate x(1), … x(nm)
Guess 𝛳 x 𝛳 x 𝛳 ...
How it works
Step 1Initialize x or with 𝛳small random values.
Step 2Minimize J(x, ) for every 𝛳 i = 1, …, nm and j = 1, …, nu
Step 3Predict movie rating with 𝛳Tx
How to get similar movies?Action (x1) Cartoon (x2)
Star War (x(1)) 1 0
Captain America (x(2))
1 0
Kung Fu Panda (x(3))
0 1
Zootopia (x(4)) 0 1
Lilo & Stitch (x(5)) 0 1Minimum value of | x(5) - x(i) |, i
from 1 - 4
Q & A
