Send the link below via email or IMCopy
Present to your audienceStart remote presentation
- Invited audience members will follow you as you navigate and present
- People invited to a presentation do not need a Prezi account
- This link expires 10 minutes after you close the presentation
- A maximum of 30 users can follow your presentation
- Learn more about this feature in our knowledge base article
Do you really want to delete this prezi?
Neither you, nor the coeditors you shared it with will be able to recover it again.
Make your likes visible on Facebook?
Connect your Facebook account to Prezi and let your likes appear on your timeline.
You can change this under Settings & Account at any time.
Transcript of Machine Learning
Computers can unconsciously evolve programs to do things that no human could consciously program......
In machine learning, information processors perform tasks of sorting,assembling,assimilating and classifying information.
Even though we programmed these algorithms,what actually happens when it unfolds live,we don't control any more.
Arthur Samuel (1959) :
Machine Learning: Field of study that gives computers the ability to
learn without being explicitly programmed.
Machine Learning and Quantum Algorithms
Tom Mitchell (1998) :
Well-posed Learning Problem: A computer program is said to
learn from experience E
with respect to some task T and
some performance measure P
, if its performance on T, as measured by P, improves with experience E.
Quantum Machine Learning
Take time logarithmic in the number of vectors, an exponential speed-up over classical algorithms ( polynomial time ).
Quantum machine learning allows enhanced privacy
Quantum Computing & Quantum computer
Quantum machine learning is about manipulating and classifying large amounts of data that is presented in arrays(rectors) and arrays of arrays(tensor products)
Quantum computers are good at manipulating vectors and tensor products in high-dimensional spaces.
The paper provides supervised and unsupervised quantum machine learning algorithms for cluster assignment and cluster finding.
Take time logarithmic in the number of dimensions, an exponential speed-up over classical algorithms ( polynomial time).
Estimating metrics between vectors in N-dimensional vector spaces
Assigning N-dimensional vectors to one of several clusters of M states
Only O(log(MN)) quantum data-base states are required to perform cluster assignment, while O(MN) are required to uncover the actual data.
The data-base user can still obtain information about the desired patterns, while the data-base owner is assured that the user has only accessed an exponentially small fraction of the data base.