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This course is going to follow the same philosophy as our past courses.
In particular, we're going to use case studies to motivate the key concepts that
we're going to teach.
But there are a number of other key features that define the way we teach our
courses in this specialization.
In particular, we teach a set of core machine learning concepts and
we do so both through our case studies
as well as using visual aids to very dramatically guide the process.
And then, in these courses following on from the foundations course, we're
going to go into details on the algorithms of the methods provided in the course.
And we're not just going to provide a laundry list of methods people use for
clustering and retrieval.
We're going to focus in on the methods we feel that are most widely used.
The most practical algorithms out there.
And the ones that give us the most skills for
learning other algorithms that might exist now or in the future.
And throughout this course,
you're going to get hands on experience with implementing these methods.
And not only are you going to implement these methods, but
you're going to do so on these real world applications.
So, you're going to get actual experience of deploying these
machine learning algorithms on data sets
that are things one might actually consider out there in the world.
And through this process you're going to just gain a lot of intuition
about the methods and there potential limitations and strengths.
And finally we also teach a set of advanced concepts in this course
that we mark as optional.
So these are videos that if you're interested in watching some of the details
that are under the hood and some of the things that we're going to describe,
you can watch these videos, but if you prefer not to that's totally fine.
You're going to get a very, very thorough overview of clustering and retrieval.
You'll still be able to implement and deploy these methods but
you might just not understand some of the proofs or the more detailed concepts but
this content is here for those that are interested in it.
Well, more specifically in this course,
we're going to go through a number of different models.
Like nearest neighbors for search.
We're going to talk about clustering as a high-level task,
an unsupervised learning task.
And we're going to talk about problemistic models for performing clusters.
Clustering like mixture models.
And then we're going to talk about a more intricate problemistic model called
latent Dirichlet allocation.
Then we are going to go through a number of algorithms associated with
these models.
KD-trees as an efficient data structure for
performing our nearest neighbor search, locality sensitive hashing,
k-means as a way of doing our clustering as well as MapReduce which we
mentioned is a means of paralyzing our algorithms to scale them up.
Expectation maximization for inference in our mixture models and
finally, Gibbs sampling for inference in our Latent Dirichlet allocation model.
And importantly, throughout the course, we're going to cover a number
of fundamental machine learning concepts that extent beyond just the concepts of
clustering and retrieval We're going to talk about distance metrics.
We're going to talk about approximation, algorithms, unsupervised learning,
probabilistic modeling, data parallel problems, and Bayesian inference.
So really a wide range of concepts covered in this course.