machine learning introduction
TRANSCRIPT
Machine Learning Pranav Prakash
Who am I?❖ Pranav Prakash
❖ VP Engg, Octo.ai
❖ Analytics, Recommendations, Predictions
❖ Co-Founder, Solidry
❖ 3D Printing, Computer Vision
❖ Engineering @LinkedIn SlideShare
❖ Search, Recommendations, Content Analysis
Our Agenda
❖ Artificial Intelligence
❖ Fundamental Ideas of Artificial Intelligence
❖ Machine Learning
❖ Categories
❖ Techniques
❖ Real Life Examples
Survey
❖ Artificial Intelligence
❖ Machine Learning
❖ Deep Learning
A.I.M.L.
D.L.
Machine Learning Trend
Source: Google Trends
Machine Learning vs Artificial Intelligence
Source: Google Trends
vs. Deep Learning
Source: Google Trends
Predict the Future
Fundamental Idea: Search
❖ Idea of Artificial Intelligence came in 1950s-60s
❖ Intelligence = “Intelligent Search” amongst the possible solutions
❖ Popular algo - Dijkstra
Image: https://www.cs.bham.ac.uk/~mdr/teaching/modules04/java2/DijkstraAlgo.html
Fundamental Idea: Artificial
❖ A - Star search
❖ f(n) = g(n) + h(n)
Fundamental Idea: Artificial
❖ A - Star search
❖ f(n) = g(n) + h(n)❖ Domain Knowledge❖ Explicitly Programmed❖ Artificial Intelligence
Fundamental Idea: Artificial❖ h(n, g) = Euclidean Distance
❖ h(n, g) = Diagonal Distance
❖ h(n, g) = Manhattan Distance
Image: http://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html
Search Revisited
Start State
Goal State
❖ Start with the best state❖ Find the next best state
(Refinement)❖ Repeat until convergence
Fundamental Idea: Optimisation
❖ Powerful
❖ Late 1990s
❖ Start with a guess
❖ Refine the guess until convergence
Start Guess
Goal State
Fundamental Idea: Optimisation
Learning a new flower❖ I learned a new flower
❖ Experience
❖ Seeing a flower
❖ Measure:
❖ Errors in recognising flower
❖ Task
❖ See a flower and recognise it
Learning (Training)
Tag Apple Fruit Apple Corporation Peach
Color Red White Red
Type Fruit Logo Fruit
Shape Oval Cut Oval Round
Features
Typical Workflow
Tag
Input Feature ExtractorFeatures
MLAlgo
Training
Input Feature ExtractorFeatures
Model Tag
Prediction
Formal Machine Learning
A computer program is said to learnfrom experience (E)with some class of tasks (T)and a performance measure (P)if its performance at tasks in T as measured by P improves with E
Categories of Learning
❖ Supervised
❖ Unsupervised
❖ Semi-Supervised
❖ Reinforcement
Supervised Learning
❖ We are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output
❖ Regression
❖ Classification
❖ Ex: Anti Spam
Unsupervised Learning❖ Allows us to approach problems with little or no idea
what our results should look like.
❖ Can derive structure from data where we don’t necessarily know the effects of the variables
❖ Clustering
❖ Ex: Photo Tagging to find out individual faces
Semi-Supervised Learning
❖ Small labelled data + Huge unlabelled data
❖ Labelled data = $$$$ + 🕰 🕰 🕰 🕰
❖ 400hrs of annotated data for 1hr of speech
❖ Ex: Infant word mapping (https://www.linkedin.com/pulse/from-baby-crying-machine-learning-james-mao)
Source: http://hnk.ffzg.hr/bibl/lrec2006/pdf/66_pdf.pdf
Reinforcement Learning
❖ Learn from self behaviour.
❖ Based on feedback from environment (Reward, Observation)
❖ Adaptive
Techniques
❖ Classification
❖ Predict class from observations
❖ Clustering
❖ Group observations into “meaningful” groups
❖ Regression (Prediction)
❖ Predict value from observations
Classification❖ Classify a document into a “predefined” category
❖ Document = Text, Image, Sound etc
❖ Popular Algorithms
❖ Naive Bayes
❖ Logistic Regression
❖ Decision Trees
❖ SVM
Clustering❖ Task of grouping a set of items in such a way that items
in same group are more similar to each other
❖ Objects are not predefined
❖ Popular Algorithms
❖ K-Means
❖ EM Clustering
❖ Affinity Propagation
Regression
❖ Prediction of a quantity given past values
❖ Popular Algorithms
❖ Linear Regression
❖ Logistic Regression
Real Life Examples
❖ Recommender Systems
❖ Learn to Rank
❖ Sentiment Analysis
❖ Object Recognition
Recommender Systems❖ Collaborative Filtering
❖ Amazon Machine Learning Library
❖ Edge Cases
Learn To Rank (LTR)❖ Used by search engines to rank
results
❖ LTR In Lucene
❖ solr-ltr, lucene-ltr, nlp4l
Sentiment Analysis
Bonanza
Object Recognition
Toolkits❖ General Purpose:
❖ Apache Mahout
❖ Apache Spark MLLib
❖ Tensor Flow
❖ FBLearner Flow
❖ Cloud
❖ IBM Watson
❖ AWS ML
❖ Azure ML
❖ H20
Advise for applying M.L.
❖ Analytics
❖ Clean data
❖ Choice of library/framework
❖ Hosted vs Managed
❖ Deployment of Models
Resources❖ https://github.com/josephmisiti/awesome-machine-learning❖ https://www.coursera.org/learn/machine-learning/home/welcome❖ https://www.slideshare.net/GaneshVenkataraman3/learn-to-rank-using-
machine-learning