AI workshop - Day 2

Traditional machine learning and deep learning are the core technologies of artificial

intelligence. There is a slight difference in the engineering process. The following steps. What

you don't need to do in deep learning is:

• Model evaluation

• Feature engineering

• Data cleaning

• Model building

Which of the following description of the validation set is wrong?

• The verification set can coincide with the test set.

• The test set can coincide with the training set

• The subset used to pick hyperparameters is called a validation set.

• Typically 80% of the training data 1s used for training and 20% 1s used for verification.

What are the common clustering algorithms?

• Density clustering

• Hierarchical clustering

• Spectral clustering

• k-means

• All the above

The model composed of machine learning algorithms cannot represent the true data

distribution function on a theoretical level. Just approach it.

• TRUE

• FALSE

What is the performance of artificial intelligence in the stage of perceptual intelligence?

• Machines begin to understand, think and make decisions like humans.

• Machines begin to calculate and transmit information just like humans.

• The machine starts to understand and understand, make judgments, and take some simple actions

Which of the following is true about unsupervised learning?

• Unsupervised algorithm only processes "features" and does tags.

• Dimensionality reduction algorithm is not unsupervised learning

• K-means algorithm and SVM algorithm belong lo unsupervised learning.

• none of the above.

The training error will continue to decrease as the model complexity increases.

• TRUE

• FALSE

Which of the following statements about supervised learning is correct?

• Decision tree is a supervised learning

• Supervised learning cannot use cross-validation for training.

• Supervised learning is a rule-based algorithm

• Supervised learning can be trained without labels

Jobs that are repetitive and require weak social skills are the easiest to be AI Replaced work.

• TRUE

• FALSE

Reducing the gap between the training error and the test error will result in over-fitting How to

prevent over-fitting?

• Cross validation

• Integration method

• Increase regularization

• Feature Engineering

• All the above.