COMP61011 Foundations of Machine Learning Gavin Brown & Ross King

This course… Wednesdays – rough schedule (may change) 0900 - 1000 Discussion sessions / quiz on assigned reading 1000 - 1230 Lectures on new material 1230 - 1330 Lunch 1330 - 1700 Lab sessions http://studentnet.cs.manchester.ac.uk/pgt/COMP61011

This course… 50% January exam - some short answer technical (including calculations) - some multiple choice 50% project (lab work) - all details in handbook No weekly lab assessments. http://studentnet.cs.manchester.ac.uk/pgt/COMP61011

Lab “Targets” Weekly targets for your lab work Level 1 – easy Level 2 – challenging Level 3 – tough! Do not count toward your final grade You’re an adult – manage your own time All targets in module handbook

Team Projects Find your partner now. Conduct a deep study of any topic in ML that you want! (some suggestions are in the handbook) Produce a 6 page report

Chapter 1

What are you?

“Learning” is a process not specific to a substrate (e.g. biological neurons) can be mechanized, with a careful definition

Machine Learning algorithms need data Predicting health of a patient needs measurements. Height Weight Systolic blood pressure Diastolic blood pressure Enzyme levels Blood sugar levels

Machine Learning algorithms need data height weight BP enzyme Health? 70 64 3 1 23 86 5 56 49 50 88 12 66 2 … “Examples” “Features” Class, or “label” Historical data in health records for example.

New person! Predicted Labels Training data + labels Learning algorithm Model Testing Data (no labels) New person! Predicted Labels

TRAINING PHASE TESTING PHASE New person! Predicted Labels Training data + labels Learning algorithm Model TESTING PHASE Testing Data (no labels) New person! Predicted Labels

ML algorithms make mistakes Predicting health. Quite a hard problem even for trained professional! Need to QUANTIFY performance of our algorithms. Learning algorithm Model

Chapter 2

Linear Models

A Problem Distinguish rugby players from ballet dancers.

A simple computer program will solve this….

where… f(x) …. is a MODEL Model Learning algorithm

The “Decision Stump” is a linear model where… “Decision Boundary” Model Learning algorithm

Model Learning algorithm

LINEARLY SEPARABLE NON-LINEARLY SEPARABLE

“Error landscape”

Training = driving lessons Testing = driving test Training data + labels Learning algorithm Model Testing Data (no labels) New person! Predicted Labels

LESSONS…. THEN THE TEST !

Evaluating a Model

Take a Break. Grab a coffee, then go to the labs. Work through the introductory lab session. Meet your Lab Helpers.