COMPUTER SUBJECT: BASIC ML
CONCEPTS
TYPE: GROUP WORK
EXERCISE/DISCUSSION
IDENTIFICATION: HOMEWORK1/MICL
COPYRIGHT: Michael Claudius
LEVEL: EASY
DURATION: 1-3 hours
SIZE: 100
lines!!
OBJECTIVE: Introduction to ML basic
REQUIREMENTS: ML Ch. 1
COMMANDS:
IDENTIFICATION: HOMEWORK1/MC
The following
assignments must be solved in groups (4-5 persons) and followed up by a short
presentation in the class later.
1. How would you define Machine
Learning?
2. Can you name four types of
problems where it shines?
3. What is a labeled training
set?
4. What are the two most common
supervised tasks?
5. Can you name four common
unsupervised tasks?
6. What type of
Machine Learning algorithm would you use to allow a robot to walk in various
unknown terrains?
7. What type of
algorithm would you use to segment your customers into multiple groups?
8. Would you
frame the problem of spam detection as a supervised learning problem or an
unsupervised learning problem?
9. What is an
online learning system?
10. What is
out-of-core learning?
11. What type of
learning algorithm relies on a similarity measure to make predic‐tions?
12. What is the
difference between a model parameter and a learning algorithm’s hyperparameter?
13. What do
model-based learning algorithms search for? What is the most common strategy
they use to succeed? How do they make predictions?
14. Can you name
four of the main challenges in Machine Learning?
15. If your model
performs great on the training data but generalizes poorly to new instances,
what is happening? Can you name three possible solutions?
16. What is a test
set, and why would you want to use it?
17. What is the
purpose of a validation set?
18. What is the
train-dev set, when do you need it, and how do you use it?
19. What can go
wrong if you tune hyperparameters using the test set?