1. COMP61011 Foundations of Machine Learning
Gavin Brown & Ross King

2. This course…
Wednesdays – rough schedule (may change)
Discussion sessions / quiz on assigned reading
Lectures on new material
Lunch
Lab sessions

3. 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.

4. 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

5. 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

6. Chapter 1

7. What are you?

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

9. 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

10. 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.

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

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

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

14. Chapter 2

15. Linear Models

16. A Problem
Distinguish rugby players from ballet dancers.

17. A simple computer program will solve this….

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

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

20. Model
Learning algorithm

21. LINEARLY SEPARABLE
NON-LINEARLY SEPARABLE

22. “Error landscape”

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

24. LESSONS….
THEN THE TEST !

25. Evaluating a Model

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