This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Deep Learning (Optional)
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來自 University of Michigan 的課程
Applied Machine Learning in Python
1981 個評分
從本節課中
Module 4: Supervised Machine Learning - Part 2
This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
與講師見面
Kevyn Collins-ThompsonAssociate Professor
School of Information
As we discussed in the first week of the course,
one of the key challenges in machine learning is finding the right
features to use as input to a learning model for a particular problem.
This is called feature engineering and can be part art, and part science.
It can also be the single most factor in doing well on a learning task.
Sometimes, in fact,
more often more important than the choice of the model itself.
We'll discuss this further in the last week of the course.
Because of the difficulty of feature engineering,
there's been a lot of research on what's called feature learning or
feature extraction algorithms that can find good features automatically.
This brings us to deep learning.
At a high-level,
one of the advantages of deep learning is that it includes a sophisticated automatic
featured learning phase as part of its supervised training.
Moreover, deep learning is called deep.
Because this feature extraction typically doesn't use just one feature learning
step, but a hierarchy of multiple feature learning layers.
Each feeding into the next.
Here's one simplified example of what a deep learning architecture might look like
in practice for an image recognition task.
In this case, digit recognition.
Recognizing a handwritten digit from zero to nine, for example.
You can see the automatic feature extraction step made up of hierarchy of
feature layers.
Each of which is based on a network that does convolution which can be thought of
as a filter for a specific pattern followed by a subsampling step,
also known as pooling that can detect a translated or
rotated version of that feature anywhere in the image.
So that features are detected properly for the final classification step,
which is implemented as a fully connected network.
The subsampling step also has the effect of reducing the computational complexity
of the network.
Depending on the properties of the object we want to predict, for example.
If we care only about the presence of the object in the image compared to let's say,
specific location, the subsampling part of the architecture may or
may not be included and this is only one example of a deep learning architecture.
The size, structure and other properties may look very different.
Depending on the specific learning problem.
This image from a paper by Honglak Lee and colleagues at the University of Michigan
shows an illustration of multilayer feature learning for face recognition.
Here there are three groups from left to right corresponding to first,
second and third stages of feature learning.
The matrix at each stage shows a set of image features with one feature per
square.
Each feature can be thought of as a detector or filter,
that lights up when that pattern is present in the underlying image.
The first layer of their deep learning architecture extracts the most primitive
low-level features, such as edges and different kinds of blobs.
The second layer creates new features from combinations of
those first layer features.
For faces, this might correspond to key elements that capture shapes of
higher level features like noses or eyes.
The third layer in turn,
creates new features from combinations of the second layer of features.
Forming still higher level features that capture typical face types and
facial expressions.
Finally, all of these features are used as input to the final
supervised learning step, namely the face classifier.
Here are the feature layers that result from training on different types of
objects, cars, elephants, chairs and a mixture of objects.
These kinds of complex features can't be learned from a small number of layers.
Advances in both algorithms and
computing power allow current deep learning systems to train architectures
that could have dozens of layers of nonlinear, hierarchical features.
It turns out that the human brain does something quite related to this when
processing visual information.
There are specific neurocircuits that first do low-level feature extraction,
such as edge detection and
finding the frequency of repeated patterns which are then used to compute more
sophisticated features to help estimate things like simple shapes and
their orientation or whether a shape is in the foreground or background.
Followed by further layers of higher level visual processing
that support more complex tasks, such as face recognition and
interpreting the motion of multiple moving objects.
On the position side, deep learning systems have achieved impressive gains and
have achieved state-of-the-art performance on many difficult tasks.
Deep learning's automatic feature extraction mechanisms also reduce the need
for human guesswork in finding good features.
Finally, with current software, deep learning architectures are quite flexible.
It could be adapted for different tasks and domains.
On the negative side,
however, deep learning can require very large training sets and computing power.
I'm not going to limit its practicality in some scenarios.
The complexity of implementation could be considered as one of the negatives of
deep learning and this is the reason that a number of sophisticated high-level
software packages have been developed
to assist in the development of deep learning architectures.
Also, despite the faces example we saw earlier which gave clear easy to
interpret features in most cases, often the features and
weights of typical deep learning systems are not nearly so easy to interpret.
That is, it's not clear why or
what features led a deep learning system to make a particular prediction.
Well, scikit-learn with the MLP classifier and MLP regressor classes provides
a useful environment to learn about and apply simple neural networks.
If you're interested in getting a deep understanding of deep learning and
the software tools required to use it,
we've provided some links to additional resources.
In particular, software packages usable with Python include Keras and
Lasagne which in turn use libraries that include TensorFlow and Theano.
Deep learning typically requires not only significant volumes of data for training,
but also significant computation.
Turns out that the processor inside video cards called GPUs are high-performance
graphical processing units are well-suited to large scale,
highly paralyzed high-performance computing.
Because they can do high key underlying like matrix multiplication very quickly.
This is because they are designed to process large volumes of data from memory
as you might do for streaming video, for example,
and they have many high speed registers that can operate in parallel on this data.
Unlike scikit-learn which cannot currently exploit GPUs,
these deep learning libraries could make full use of GPU clusters for
large scale learning of deep learning architectures.
