Office Hours

• DAF: Mon 10-11 and Fri 2:30 - 3:30
• Jason: Mon 11-12 and Tues 1:30 - 2:30
• Jiajun: Tues 2:30 - 3:30 and Wed 5 - 6
• Anirud: Wed 1-2 and Thur 4 - 5
• Tanmay: Mon 4 - 5 and Fri 4 - 5

Video Lectures

• I am away 1 Feb, 3 Feb. These videos are what I would have said. Watch them

  • Mean and Covariance and how they transform
  • Seeking low D structure in high D data
  • Principal Components Analysis and examples

• I am away 8 Mar, 10 Mar. These videos are what I would have said. Watch them

  • Multivariate normal distributions Note: there is at least one voice typo here, for which I apologize. I say the constant in the normal distribution is chosen so that it integrates to zero; this is (a) silly and (b) incorrect. It's chosen so that the normal integrates to one.
  • EM for Mixtures of Gaussians
  • EM for Topic Models
  • Some remarks on EM

  Announcements

  • I'm travelling 23, 24 Jan, so you won't find me at OH then: DAF

  or swing by my office (3310 Siebel) and see if I'm busy

  Evaluation is by: Homeworks and take home final.

  I will shortly post a policy on collaboration and plagiarism

   

   

  Entrance survey

  please complete this. I intend it to be anonymous (but don't fully understand google forms, so...) and it will help me know what you know already. Please don't fill in lots of forms to bias, etc.

  Advice:

  Read the textbook. I wrote it specifically for this course, AND it's free. I will split time in lecture between sketching important points described in the text, and solving problems. If you haven't read the text, this might be quite puzzling.

  Required Text:

  Applied Machine Learning Notes, D.A. Forsyth, (approximate 9'th draft)

  • Version of 18 Jan 2017
  • Version of 17 Mar 2017
  • Version of 28 Mar 2017
  • Version of 6 April 2017

  Piazza link

  for this course (which is now right, I believe)

  I'm a video star! (or at least, I have been filmed)

  • you can see me here

  Backup Material:

  Probability and Statistics for Computer Scientists, D.A. Forsyth, (approximate 12'th draft)

  • Version of 21 Dec 2016

   

  Notes I made in class:

  • a figure for discussion of mean field inference

  Code fragments I showed in class:

  I've cleaned some of these up a bit, but they're not intended to be production code, etc; just to show some R tricks
  • A naive bayes classifier on the Pima indians dataset; I averaged over 10 test train splits, and ignored examples with NA values; mainly interesting for simple code tricks. File here.
  • A naive bayes classifier on the Pima indians dataset; I averaged over 10 test train splits, but now I used examples with NA values both in train and test; mainly interesting for simple code tricks. File here.
  • A naive bayes classifier on the Pima indians dataset, using Klar and Caret; mainly interesting for simple code tricks. File here.
  • An SVM on the Pima indians dataset, using Klar and Caret and SVMLight; mainly interesting for simple code tricks. File here.
  • A much more elaborate SVM on the Pima indians dataset, using Klar and Caret and SVMLight. File here.

  Homeworks:

  • Homework 1, due 23h59 30 Jan 2017
  • Homework 2, due 23h59 6 Feb 2017
  • Homework 3, due 23h59 20 Feb 2017
  • Homework 4, due 23h59 6 Mar 2017
  • Homework 5, due 23h59 20 Mar 2017
  • Homework 6, due 23h59 10 Apr 2017 Warning! lots of time available for this one, because history shows it's hard. Notice! Figures now on page
  • Homework 7, due 23h59 17 Apr 2017 Notice! This is much easier than it looks.
  • Homework 8, due 23h59 8 May 2017 This is the take home final for the course.
  • Homework 9 (optional; remission of sins), due 23h59 8 May 2017 This is the remission of sins homework for the course.

   

  R resources:

  • An R Tutorial, prepared by Karthik Sridharan with revisions by Henry Lin
  • An introduction to R
  • Resources to help you learn and use R, from UCLA
  • R tutorials, from W.B. King, Coastal Carolina University
  • R code school, where you can get badges (?!?)