machine learning. quotes for machine learning “a breakthrough in machine learning would be worth...
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Machine Learning
Quotes for Machine learning
“A breakthrough in machine learning would be worthten 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)
Traditional Programming
Machine Learning
ComputerComputerData
ProgramOutput
ComputerComputerData
OutputProgram
So What is Machine Learning?
• Getting computers to program themselves• Writing software is the bottleneck• Let the data do the work instead!
Applications• Web search • Computational biology• Finance• E-commerce• Space exploration• Robotics• Information extraction• Social networks• Debugging• [And your favorite area]
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
Representation
• Decision trees• Sets of rules / Logic programs• Instances• Graphical models (Bayes/Markov nets)• Neural networks• Support vector machines• Model ensembles• Etc.
Evaluation
• Accuracy• Precision and recall• Squared error• Likelihood• Posterior probability• Cost / Utility• Margin• Entropy• K-L divergence• Etc.
Optimization
• Combinatorial optimization– E.g.: Greedy search
• Convex optimization– E.g.: Gradient descent
• Constrained optimization– E.g.: Linear programming
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
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)
Supervised Learning- Application
• Bio/che informatics• Handwriting recognition• OCR• Spam detection• Pattern recognition• Speech recognition
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
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)
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
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
ML is Just like gardening?
Seeds
Nutrients
Gardener
Plants
You
Data
Algorithms
Programs
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, ...
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
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