Pranav Prakash is a VP of engineering who has worked on projects involving machine learning, computer vision, and recommendations. The document discusses fundamental concepts in artificial intelligence including intelligent search algorithms. It covers categories of machine learning such as supervised, unsupervised, and reinforcement learning. Popular machine learning techniques like classification, clustering, and regression are described. Real-life applications of machine learning like recommender systems, sentiment analysis, and object recognition are also mentioned.

1. Machine Learning Pranav Prakash

2. 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

3. Our Agenda
❖ Artificial Intelligence
❖ Fundamental Ideas of Artificial Intelligence
❖ Machine Learning
❖ Categories
❖ Techniques
❖ Real Life Examples

4. Survey
❖ Artificial Intelligence
❖ Machine Learning
❖ Deep Learning
A.I.
M.L.
D.L.

6. Machine Learning Trend
Source: Google Trends

7. Machine Learning vs Artificial Intelligence
Source: Google Trends

8. vs. Deep Learning
Source: Google Trends

9. Predict the Future

10. 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

11. Fundamental Idea: Artificial
❖ A - Star search
❖ f(n) = g(n) + h(n)

12. Fundamental Idea: Artificial
❖ A - Star search
❖ f(n) = g(n) + h(n)
❖ Domain Knowledge
❖ Explicitly Programmed
❖ Artificial Intelligence

13. 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

14. Search Revisited
Start State
Goal State
❖ Start with the best state
❖ Find the next best state
(Refinement)
❖ Repeat until convergence

15. Fundamental Idea: Optimisation
❖ Powerful
❖ Late 1990s
❖ Start with a guess
❖ Refine the guess until
convergence
Start Guess
Goal State

16. Fundamental Idea: Optimisation

17. 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

18. Learning (Training)
Tag Apple Fruit Apple Corporation Peach
Color Red White Red
Type Fruit Logo Fruit
Shape Oval Cut Oval Round
Features

19. Typical Workflow
Tag
Input Feature Extractor
Features
ML
Algo
Training
Input Feature Extractor
Features
Model Tag
Prediction

20. Formal Machine Learning
A computer program is said to learn
from 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

21. Categories of Learning
❖ Supervised
❖ Unsupervised
❖ Semi-Supervised
❖ Reinforcement

22. 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

23. 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

24. 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

25. Reinforcement Learning
❖ Learn from self behaviour.
❖ Based on feedback from
environment (Reward,
Observation)
❖ Adaptive

26. Techniques
❖ Classification
❖ Predict class from observations
❖ Clustering
❖ Group observations into “meaningful” groups
❖ Regression (Prediction)
❖ Predict value from observations

27. Classification
❖ Classify a document into a “predefined” category
❖ Document = Text, Image, Sound etc
❖ Popular Algorithms
❖ Naive Bayes
❖ Logistic Regression
❖ Decision Trees
❖ SVM

28. 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

29. Regression
❖ Prediction of a quantity given past values
❖ Popular Algorithms
❖ Linear Regression
❖ Logistic Regression

30. Real Life Examples
❖ Recommender Systems
❖ Learn to Rank
❖ Sentiment Analysis
❖ Object Recognition

31. Recommender Systems
❖ Collaborative Filtering
❖ Amazon Machine Learning Library
❖ Edge Cases

32. Learn To Rank (LTR)
❖ Used by search engines to rank
results
❖ LTR In Lucene
❖ solr-ltr, lucene-ltr, nlp4l

33. Sentiment Analysis

34. Bonanza

35. Object Recognition

36. Toolkits
❖ General Purpose:
❖ Apache Mahout
❖ Apache Spark MLLib
❖ Tensor Flow
❖ FBLearner Flow
❖ Cloud
❖ IBM Watson
❖ AWS ML
❖ Azure ML
❖ H20

37. Advise for applying M.L.
❖ Analytics
❖ Clean data
❖ Choice of library/framework
❖ Hosted vs Managed
❖ Deployment of Models

38. 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