S1 Week 9 - Introduction to supervised learning - Nearest Neighbours

I (David) am lecturing this week. I like to write on the slides during the lectures, particularly where there are equations or diagrams. I recommend reading the lecture notes before the lectures, and then taking notes during the lecture, or annotating the slides that I've provided.

Monday's lecture: Supervised learning - classification with Nearest Neighbours 

• Reading: Lecture notes Chapter 11
• Slides released before Monday's lecture (to annotate)
• Slides as annotated during Monday's lecture

Wednesday's lecture: k-Nearest Neighbours and evaluation

• Reading: Lecture notes Chapter 12
• Slides released before Wednesday's lecture (to annotate)
• Slides annotated during Wednesday's lecture

Comprehension questions‏

• There is one set of comprehension questions in Learn for both lectures, which will be released on Wednesday.   
• For some questions there are solutions: fds-s1-09-1-comprehension-question-solutions.pdf

Workshop

This week's workshop is designed to help you learn about applications of linear regression and critiquing data science studies - something that we will assess in the exam.

Lab

This week's Lab complements the lectures on k-Nearest Neighbours.