[MUSIC] 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.