CONTENT VERY SIMILAR TO 2019/2020 EXAM. Heard from many students that they passed the exam with the help from this document! Thanks for the feedback. Also have a look at my cheatsheet including code!
A complete overview of all lectures, including slide snippets and additional notes for the course...
, Lecture 1: Intro
Learning from examples is done by fnding examples of two classes to predict, for example
spam and non-spam. Based on this, we can come up with a learning algorithm, which infers
rules from existng examples. Eventually, we want these rules to be applied to new data and
to give the classifcatonooutcome. In this case spamono-spam.
In this course, we will apply some basic ML techniques and familiarize outselves with SciKit-
learn, the leading Python package for ML. (others are BigML and MLlib). Some examples
that use Machine learning are:
Recognizing handwriten text
Recognize faces in photos
Determine whether a text has a positve or negatve sentment
Flagging suspicious Credit Card actvity
Recommend booksomovies (Netlix)
Let’s look at the main types of learning.
Regression: We want to receive a real number (for example a house price, a grade,
or a stock price). We measure error for predicton with MSE or RMSE.
Binary classifcaton: We want to classify using two possibilites (YesoNO, SpamoNo-
spam, positveonegatve sentment). We measure error with the proporton of
mistake, thus with precision and recall, or even the F1-score.
o Precision: What fracton of fagged emails as Spam were actually SPAM?
o Recall: What fracton of real SPAM was fagged?
o F-score: Harmonic mean between precison and recall.
Multclass classifcaton: Classify into multple (but fnite) set of classes (for example,
newspapers categories or detectng animal species). For multclass, it’s hard to
measure using Recall and precision. Therefore we simplify it a bit.
Ranking: We rank according to relevance (for example, this is what Google does with
webpages).
Autonomous behaviour. We can learn a car to react on input (for example sensory
data, camera, microphones) and take actons (brake, steer, etc).
How do we know that an algorithm is learning? Let’s suppose we’re studying for an exam.
First we train, based on the sheets we infer knowledge. Than, we potentally make a test-
exam, which is something we use to determine our progression (development set). Then,
we go to the fnal exam (which is obviously not accessible in advance), which is our test set.
Machine learning studies the algorithms which can learn t solve problems from examples.
Several canocial problem types, including regression (mult-)classifcaton and ranking. The
frst step is always to decide on our data split and how to evaluate (PrecisionoRecall, or
RMSE).
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller YorranSlik. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $5.90. You're not tied to anything after your purchase.
