# online course material 2012 archive

**Lecture slides**

- Lecture 1
- Lecture 2

Supplemental notes on Binary Markov Chain derivation. - Lecture 3
- Lecture 4

Supplemental notes on Decision theory perspective on classification - Lecture 5

Supplemental notes on Least squares derivation and the pseudoinverse; bed-time reading on the Moore-Penrose pesudoinverse (optional) - Lecture 6

Supplemental (but vital) notes on Principal Components Analysis - Lecture 7

Demonstration of paper presentation, on "Neurosynth" Yarkoni et al, Nature Methods Nature Methods, 8(8):665–670, 2011. - Lecture 8

**Labs**

- Lab 1: Lab01.pdf

Solutions: Lab01_files.zip - Lab 2: Lab02.pdf

Solutions: Lab02_files.zip - Lab 3: Lab03.pdf

Files: spam_data.mat

Solutions: Lab03_files.zip - Lab 4: Lab04.pdf Lab04_hints.pdf

Files: digits.mat display_digit.m

Solutions: Lab04_files.zip - Lab 5: Lab05.pdf

Files: Lab05.m GetDat.m Train.mat Test.mat

Solutions: Lab05_files.zip - Lab 6: Lab06.pdf Lab06_pseudocode.m

Files: digits.mat

Solutions: Lab06_files.zip - Lab 7: Lab07.pdf

Files: NYtimes.mat

Solutions: Lab07_files.zip - Lab 8: Lab08.pdf Lab08_hints.pdf (including some figures of the 'solution')

Files: (see solutions zip)

Solutions: Lab08_files.zip - Lab 9: Lab09_CourseReview.pdf

**Written Assignment**

**Problem Sets**

- HW01.pdf (Roughly corresponds to Lecture 1)

Solutions: HW01_Sol.pdf - HW02.pdf (Roughly corresponds to Lecture 2)

Solutions: HW02_Sol.pdf - HW03.pdf (Roughly corresponds to Lectures 3 & 4)

Solutions: HW03_Sol.pdf - HW04.pdf (Roughly corresponds to Lectures 4 & 5)

Solutions: HW04_Sol.pdf - See Lab09_CourseReview.pdf for more review problems.

**Other resources:**

**Critical reading assignment**

- List of potential papers for presentation.
- Presentation Tips
- Polished, practiced and organised presentation
- Slides without too much text (short phrases, not full sentences)
- Ample but purposeful use of illustrations/graphics/figures
- Clear statement of contributions and weakness of the paper
- Enough background to make the goal/importance of the paper clear

**MATLAB**

**Matrices**

**An introduction to matrix derivatives****A useful matrix "cheat sheet**" (from Sam Roweis, Toronto)- Two exhaustive references:
**Matrix Cookbook**(online book by Petersen & Pedersen) and**Matrix Reference Manual**(web-book by Mike Brookes).