Machine learning Flashcards
How would you define machine learning?
Machine learning is about building systems that can learn from data.
Learning means getting better at some task, given some performance measure.
Can you name four types of problems where Machine Learning shines?
1) Machine learning is great for complex problems for which we have no algorithmic solution,
2) to replace long lists of hand-tuned rules,
3) to build systems that adapt to fluctuating environments,
4) and finally to help humans learn.
What is a labelled training set?
A labelled training set is a training set that contains the desired solution (a.k.a. a label) for each instance.
What are the two most common supervised tasks?
The two most common supervised tasks are:
1) regression
2) classification
Can you name four common unsupervised tasks?
Common unsupervised tasks include clustering, visualization, dimensionality reduction, and association rule learning.
What type of Machine Learning algorithm would you use to allow a robot to walk in various unknown terrains?
Reinforcement learning is likely to perform best if we want a robot to learn to walk in various unknown terrains.
What type of algorithm would you use to segment your customers into multiple groups?
If you don’t know how to define the groups, then you can use a clustering algorithm (unsupervised learning) to segment your customers into clusters of similar customers. However, if you know what groups you would like to have, then you can feed many examples of each group to a classification algorithm (supervised learning), and it will classify all your customers into these groups.
Would you frame the problem of spam detection as a supervised learning problem or an unsupervised learning problem?
Spam detection is a typical supervised learning problem: the algorithm is fed many emails along with their labels (spam or not spam).
What is an online learning system?
An online learning system can learn incrementally, as opposed to a batch learning system. This makes it capable of adapting rapidly to both changing data and autonomous systems, and of training on very large quantities of data.
What is out-of-core learning?
Out-of-core algorithms can handle vast quantities of data that cannot fit in a computer’s main memory. An out-of-core learning algorithm chops the data into mini-batches and uses online learning techniques to learn from these mini-batches.
