Machine Learning Foundations:
A Case Study Approach
Machine Learning
Pipeline
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Case Study 1:
Predicting House Prices
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- Prediction of house sales price
- Looking at other houses and their house sales prices to inform the house valueof this house we're interested in.
- Looking at other features of the houses e. g. number of square feet
Case Study 2:
Sentiment Analysis
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- Looking at the text of
the review and the rating of the review, in order to understand what's the relationship for classification of the sentiment.
- Analyzinng the text of this
review in terms of how many
times it uses the word
Case Study 3:
Document Retrieval
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- In this case,data that we have is a
huge collection of possible articles
- intelligence we're deriving is an
article or a book that's of interest to
our reader
- findind structure in this data based
on groups of related articles
Case Study 4:
Product Recommendation
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- taking your past purchases +
other user's purchurse histories and
trying to use those to recommend
some set of other products
you might be interested in purchasing
- Using features of all customers and
features of products and trying to
compare and match those features
with your features
Case Study 5:
Visual Product Recommener
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- Here inputs are Images not Texts!
- Outputs are a set of results of shoes
that might also be of interest to a customer
and this customer want to be able to
search over those to purchase an Item
- In order to be able to go from an
image to a set of related images, we
need to have very detailed features
about that image to find other images
that are similar
- Deep Learning
- Each layer of the neural network provides
more and more descriptive features
Process of Machine Learning
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- Which task should be done do, e. g.
solving a sentiment analysis problem
- Which machine learning models should be
used, e. g. Support Vector Machine or
Regression
- Which methods should be used to
optimize the parameters of the model?
- An finally: the following questions:
Is this model really providing the
intelligence that I'm hoping for?
How do we measurethe quality of the system?
Going into depth in different Areas
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Which models or Technics should be used for specific Task?
Which Algorithms should be used for Optimizing the model?
Which key machine Learning Concepts should be used?
Regression
Case Study: Predictiong house prices
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Classification
Case Study: Analyzing Sentiment
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Clustering and Retrieval
Case Study: Finding Documents
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Matrix Factorization & Dimentiality Reduction
Case Study: Recommending Products
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