update class 14 materials

homework/14_spam_filtering.md

@@ -5,7 +5,20 @@ Read Paul Graham's [A Plan for Spam](http://www.paulgraham.com/spam.html).
 Here are some questions to think about:
 
 - Should a spam filter optimize for sensitivity or specificity, in Paul's opinion?
+    - Specificity, in order to minimize false positives (non-spam being incorrectly marked as spam).
 - Before he tried the "statistical approach" to spam filtering, what was his approach?
+    - He hand-engineered features and used those features to compute a score.
 - What are the key components of his statistical filtering system? In other words, how does it work?
+    - Scan the entire text (including headers) and tokenize it.
+    - Count the number of occurrences of each token in the ham corpus and the spam corpus (separately).
+    - Assign each token a "spam score" based on its relative frequency in the corpora.
+    - For new email, only take into account the 15 most "interesting" tokens.
 - What did Paul say were some of the benefits of the statistical approach?
+    - It works better (almost no false positives).
+    - It requires less work because it discovers features automatically.
+    - The "spam score" is interpretable.
+    - It can easily be tuned to the individual user.
+    - It evolves with the spam.
+    - It creates an implicit whitelist/blacklist of email addresses, server names, etc.
 - How good was his prediction of the "spam of the future"?
+    - Great!