machine learning in the age of big data: new approaches and business applications
Machine Learning: finding features, patterns &
The connectionist approach: Neural Networks
The Deep Learning “revolution”: a step closer to the brain?
The Big Data deluge: better algorithms & more data
Was “Deep Blue” Intelligent?
How about Watson?
Does machines have reached the intelligence
level of a rat?
Let’s be pragmatic: I’ll call “intelligent” any
device capable of surprise me!
1943 – Mculloch& Pitts + Hebb
1968- Rosenblatperceptron and the Minsk
argument - or why a good theory may kill an even better idea
2006- Hinton Deep Learning (Boltzmann)
All together: Watson, Google et al
Input builds up on receptors
Cell has an input threshold
Upon breech of cell’s
threshold, activation is fired
down the axon.
ANN are very hard to optimize
Lots of local minimum (trap for stochastic
Permutation invariant (no unique solution)
How to stop training?
Neural Networks are incredible powerful
But they are also wild beasts that should be
treated with great care
Its very easy to fall in the GIGO trap
Problems like overfit, suboptimization, bad
conditioned, wrong interpretation are
Interpretation of outputs
Outputs ≠ probabilities
Where to draw the line?
VERY careful in interpreting the outputs of ML
algorithms: you not always get what you see
Clean & balance the data
Normalize it properly
Remove unneeded features, create new ones
Rutherford Backscattering (RBS)
Credit Risk & Scoring
Churn prediction (CDR)
Prediction of hotel demand with Google trends
Ion beam analysis
25Å Ge -layer under 400 nm Si
Angle of incidence
Yield (arb. units)
Neural networks are good when: many
training data available; continuous
variables; relevant features are known;
unicity of the mapping.
Neural networks are less useful when:
problem is linear; few data compared to
the size of search space; data high
dimensional; long range correlations.
They are black boxes
Artificial neural networks
realised concepts, rules, calculations
Concepts, images, categories,
between Programmed a priori
Through a limited set of Self-programmable (given an
Through internal algorithmic Continuously adaptable
Tolerance to errors
By examples (analogies)
ANN are massive correlation & feature
extraction machines isn’t what intelligence is all about?
Knowledge is embedded in a messy network
Capable to model an arbitrary complex
We need thousands of examples for training.
Algorithms are simple: complexity lies in the
“quasi” non-supervised machines
Extract and combine subtle features in the data
Build high-level representations (abstractions)
Capable of knowledge transfer
Can handle (very) high-dimensional data
Are deep and broad: millions of synapses
Work both ways: up and down
Learning features that are not mutually exclusive
Top on image identification (is some cases it
Top on video classification
Top on real-time translation
Top on Gene identification
Reverse engineering: can replicate complex
human behaviour, like walking.
Data visualization and of text disambiguation
Most ML algorithms work better (sometimes
much better) by simple throwing more data
And now we have more data. Plenty of it!
Which is signal and which is noise? Let the
machines decide (they are good at it)
Where humans stands in this equation? We
are feeding the machines!
Don’t look for causation; welcome correlations
Messiness - prepare to get your hands dirty
Don’t expect definitive answers. Only
communists have them!
Stop searching God’s equation
Keep theories at bay and let the data speak
Exactitude may not be better than “estimations”
Forget about keep data clean and organized
Data is alive and wild. Don’t imprisoned it
Netflix movie rating contest
New York city building security
Prediction rare events frauds and why its
A step closer to the brain? Yes and No
What is missing?
Predictive analytics (crime before it occurs)?
Algorithms that learn & adapt
Big Data & algorithms are revolutionizing the
Recommendations (Amazon, Netflix, Facebook)
Trading (70% Wall Street is made by them)
Identifying your partner, recruiting, votes
Images, video, voice, translation (real time)
Where are we heading?
Hinton Google talks
“Too big to know”
Big Data: a new revolution that will transform
Machine Learning in R
Matlab (several code – google for it)
R (CRAN repository), Rminer
C++ (mainly on Github)
More on Deeplearning.net
Recommend an unseen item i to an user ubased on engagement of other users to items 1 to 8.
Items recommended in this case are i2 followed by i1.
Item based recommendation for a user ua
based on a neighbour of k = 3.
Items recommended in this case
are i3 followed by i4.
(item based CF superior to user based CF
but it requires lot of information like ratings
or user interaction with the product).