1. Machine Learning

2. Quotes for Machine learning
“A breakthrough in machine learning would be worth ten Microsofts” (Bill Gates, Chairman, Microsoft)
“Machine learning is the next Internet” (Tony Tether, Director, DARPA)
“Machine learning is the hot new thing” (John Hennessy, President, Stanford)
“Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo)
“Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo)
“ What are all the important questions in Machine Learning”
( Student(X) , Pre-Final year B.Tech-Cse, SRM University)

3. Traditional Programming
Machine Learning
Computer
Data
Output
Program
Computer
Data
Program
Output

4. So What is Machine Learning?
Getting computers to program themselves
Writing software is the bottleneck
Let the data do the work instead!

5. Applications Web search Computational biology Finance E-commerce
Space exploration
Robotics
Information extraction
Social networks
Debugging
[And your favorite area]

6. ML is growing growing…
More than tens thousands of machine learning algorithms
Hundreds are adding every year
Every machine learning algorithm has three components:
Representation
Evaluation
Optimization

7. Representation Decision trees Sets of rules / Logic programs Instances
Graphical models (Bayes/Markov nets)
Neural networks
Support vector machines
Model ensembles
Etc.

8. Evaluation Accuracy Precision and recall Squared error Likelihood
Posterior probability
Cost / Utility
Margin
Entropy
K-L divergence
Etc.

9. Optimization Combinatorial optimization Convex optimization
E.g.: Greedy search
Convex optimization
E.g.: Gradient descent
Constrained optimization
E.g.: Linear programming

10. Types of Machine Learning
Supervised (inductive) learning
Training data includes desired outputs
Unsupervised learning
Training data does not include desired outputs
Semi-supervised learning
Training data includes a few desired outputs
Reinforcement learning
Rewards from sequence of actions

11. Supervised Learning
It is the task of inferring a function from labeled data. The trained data consist of set of trained examples. The supervised learning analyzes the trained data and produces an inferred function (which can be used for new samples)

12. Supervised Learning- Application
Bio/che informatics
Handwriting recognition
OCR
Spam detection
Pattern recognition
Speech recognition

13. Unsupervised learning
The examples given to the learner are unlabeled data. There is no wrong or improper values to achieve the optimized solution.
Clustering (K means , hierarchical clustering)
Hidden markov models
Self organizing maps (AOM)
Adaptive resonance theory (ART)
Etc

14. Semi – supervised Learning
A sub class of supervised learning, taking small amount of labeled data and large amount of unlabeled data.
It falls between unsupervised learning
( without any labeled training data) and supervised learning (completely labeled training data).
Example : heuristic function (From the calulated distance between two cities to discovers the distance between known to unknown cities)

15. Reinforcement Learning
In the Markov decision processed environments, the dynamic programming techniques should apply.
It differs from slandered supervised learning in that correct input /output pairs are never presented.
Q-Learning algorithm
Sarsa algorithm

16. ML in Practice Understanding domain, prior knowledge, and goals
Data integration, selection, cleaning, pre-processing, etc.
Learning models
Interpreting results
Consolidating and deploying discovered knowledge
Loop

17. ML is Just like gardening?
Seeds
You
Nutrients
Algorithms
Gardener
Data
Plants
Programs

18. Current “hot” practical issues
Learning from labeled and unlabeled data.
Nonlinear embeddings.
MDPs and stochastic games.
Adaptive programs and environments.
Kernels, comp bio, many others, ...

19. Learning from labeled and unlabeled data
Often unlabeled data is cheap, labeled data is expensive.
unlabeled data can suggest which hypothesis are a-priori more reasonable.
Algorithmically, can use to bootstrap.
E.g., e.g., words on page; words pointing to page

20. END