Welcome back. We'll now begin our lectures again with
Learn. We'll start by revisiting the concept of
learning which we studied during the listen lecture where we learned about the
naive base classifier. I will try to put that on a more firmer,
formal footing as well as, Make it generalized to other forms of
learning. In particular, unsupervised learning where
we don't necessarily have a training set like we did earlier.
We'll learn how to learn business rules or other kinds of rules from data.
We'll also figure out how to learn the classes and features themselves, and study
how this happens in recommender systems such as book recommendations, product
recommendations, movie reviews, and stuff like that on the web.
We will also speculate on how this tells us something about how human beings learn
basic concepts about the world without ever being taught explicitly what features
to look for. Next, we'll turn to supervised learning
again where we'll figure out how to learn facts, such as, antibiotics kill bacteria,
or Einstein was born in Ulm, and other things that we can learn formally from
large collections of text such as that is available on the web.
We'll then speculate on whether knowledge learned in this manner.
Whether it's by, by crawling through large volumes of text.
Or learning classes an features from data or rules.
Actually, constitutes knowledge of any kind and, philosophically, does it give us
some satisfaction that we've figured out something about how we all learn so much
about the world. So, gets, get ready.
We'll be delving deeper and covering a large tract of material this week.
We'll also be doing things a bit more formally, a little more notation, some
mathematical notation. But, for those of you who get confused,
hopefully you won't. But, please remember that in the end, it's all about counting.
So, don't get scared of the notation. Be careful to remember what it's all
about. Counting, frequencies, probabilities, and
essentially, Arithmetic.
So, Let's get started.