The module content closely follows “An Introduction to Statistical Learning: With Applications in R”, which is the simplified version of “The Elements of Statistical Learning”; both books are written by the same group of authors. ST3248 covers chapter 1 through 6 of the textbook, ST4248 picks up from there until chapter 12, and both are semester-exclusive. Also, there is a 60% content overlap between ST3248 and ST3131.
The topics are quite simple, so long as you read the book or attend lectures you should be strolling along well. You need to learn some R in this module as well for both labs and homework, but exams will only require you to be able to decipher the R code output so don’t stress out.
Assessment is straightforward. There are 5 take-home assignments (40%), one every two weeks. They are graded on S-U basis and may take a bit of time (~2-3hr each) to do, but honestly are not difficult. The final exam paper makes up 60% and is more challenging. Do revise your ST2132 beforehand.
Prof Nott is very nice. His lectures are clear and thorough, and administrative matters are well communicated. He also sets good paper.
Overall, this is a manageable module with super light workload, so can be taken if your semester is already packed. I would recommend part II as well the actual ESL book for further exploration (:
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