Machine Learning Prague 2016 www.mlprague.com

1. Email.cz workshop
Vit Listik @tivwvit

2. Email stats
● 60M emails per day
● 3M users daily, 6M monthly
● 2 PB
Email delivery process

3. Antispam
● Fighting with the bad guys

4. Antispam sources
● Content
○ Text
○ Images
○ Attachments
○ Links
○ Headers
● Metadata
○ Traffic
○ Historic data (reputation)
○ Blacklists
○ Rules (DKIM, DMARC, SPF)

7. Grey email
Graymail is solicited bulk email messages that don't fit the definition of email spam (e.g., the recipient "opted into" receiving them). Recipient
interest in this type of mailing tends to diminish over time, increasing the likelihood that recipients will report graymail as spam. In some
cases, graymail can account for up to 82 percent of the average user's email inbox.

8. Antispam stats again

9. ML in antispam
● Topic
● Usubscribe
● Phishing
● Domain keywords
● Images
● Personalized filter
● Link naturalness

10. Examples
https://github.com/tivvit/ML-Prague-2016-email-workshop

11. Tools
● Jupyter
○ Visualizations
○ State
● HDF5
● Pandas
● Ipython cluster
● Cluster storage

12. Let's go (to) Jupyter

14. Topic categorization
● 16 categories
● Manually labeled dataset
● 2 languages (2 models)
● 7th version
● Overlapping classes

15. NLP
● Bag of words
● Lemmatization
● Stop words
(1) John likes to watch movies. Mary likes movies too.
(2) John also likes to watch football games.
[
"John",
"likes",
"to",
"watch",
"movies",
"also",
"football",
"games",
"Mary",
"too"
]
(1) [1, 2, 1, 1, 2, 0, 0, 0, 1, 1]
(2) [1, 1, 1, 1, 0, 1, 1, 1, 0, 0]

16. SVM
● Classification
● Best split for classes
● Linear classifier (kernels)
Multi class version
● One vs. all
● Winner takes all

17. Topic categorization

18. Image categorization
● Classes: spam x ham
● Based on user reaction
● Links analysis
● Low level image features
○ Size
○ DPI
○ Hists
○ Exif
○ Compression
● Raw pixels

19. Spam roulette

21. User reactions
● Noisy
● Inconsistent
● Bots
● Low ratio

22. Image topics
● Caffe
● Pretrained network
● Same classes as for words
● Cleaned dataset of images from classified emails
● 400k images
● Slow on CPU
Loan non-bank Pharmacy DiscountEbola

23. Distributed learning
● Spark
● SparkNet (Caffe)
● Elepheas (Keras)

24. Image types
● Trivial
○ Animated
○ Monitoring
○ Border
● Photo
● Graphics
● Photo with graphics
Graphics
Photo

25. Image features
Extraction
● PIL
● OpenCV
● Image Magick
Features (142)
● Channel stats
○ Min, max, mean
○ Standard deviation
○ Skewness
○ Entropy

26. Learning
● Scipy - Decision Trees
● Keras (Tensorflow, theano)
● 30k Manually labeled samples

27. Trees vs. neurons

28. Message
● Gray email
● Explore (visualize) your data (in Jupyter)
● Use libraries
● Simple subtasks (boosting) may help
● Store intermediate results
● Store test results with the model