UNIT 1

Well defined learning problems

Q1. Explain well-defined learning problems

Solution: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance in tasks T, as measured by P, improves with experience E.

Features in a Learning Problem

Examples of Well Defined Learning Problem

Checkers Learning Problem

Handwriting Recognition Learning Problem

Robot Driving License Problem

Q2. Give a stepwise explanation of the find-S algorithm

Solution: The step of the find-S algorithm:

Q3. Explain the Candidate Elimination Algorithm in detail.

Solution: The Candidate Elimination Algorithm -

- If d is a positive example
- Remove from G any hypothesis that does not include.
- For each hypothesis s in S that does not include d, remove s from S.
- Add to S all minimal generalizations h of s such that h includes d, and
- Some member of G is more general than h
- Remove from S any hypothesis that is more general than another hypothesis in S.

- If d is a negative example
- Remove from S any hypothesis that does not include.
- For each hypothesis g in G that does not include d
- Remove g from G

- h does not include d and
- Some member of S is more specific than h

Q4. Differentiate b/w classification, regression, and clustering & rule extraction

Solution: Classification

Regression

Clustering

Rule Extraction

Q5. Explain the disadvantages and advantages of different ML algorithms

Solution:

Advantages of Supervised Machine Learning Algorithms

Disadvantages of Supervised Machine Learning Algorithms

Advantages of Unsupervised Machine Learning Algorithms

Disadvantages of Unsupervised Machine Learning Algorithms

Advantages of Semi-supervised Machine Learning Algorithms

Disadvantages of Semi-supervised Machine Learning Algorithms

Advantages of Reinforcement Machine Learning Algorithms

Disadvantages of Reinforcement Machine Learning Algorithms

Q6. Elaborate steps involved in building a Machine Learning System

Solution: Following are the various steps involved in building a Machine Learning System –

Q7. Elaborate steps involved in The LIST-THEN-ELIMINATE Algorithm.

Solution: The LIST-THEN-ELIMINATE Algorithm:

Following are the steps for the LIST-THE-ELIMINATE algorithm:

For each training example, <x, c(x)>

Q8. Elaborate on what Is Learning?

Solution: There are several definitions of "learning." One of the simplest definitions is:

“The activity or process of gaining knowledge or skill by studying, practicing, being taught, or experiencing something.”

Just as there are various definitions of learning, there are various categories of learning methods.

As a human, we learn a lot of things throughout our life. Some of them are based on our experience and some of them are based on memorization. Based on that, we can divide learning methods into five parts:

Inductive learning is based on formulating a generalized concept after observing examples of the concept. For example, if a kid is asked to write an answer to 2*8=x, they can either use the rote learning method to memorize the answer or use inductive learning (i.e. thinking how 2*1=2, 2*2=4, and so on) to formulate a concept to calculate the results. In this way, the kid will be able to solve similar types of questions using the same concept.

Similarly, we can make our machine learn from past data and make them identify whether an object falls into a specific category of interest.

Q9. Write a short note on Inductive Learning Hypothesis

Solution: Inductive Learning Hypothesis

The ultimate goal of concept learning is to identify a hypothesis H identical to target concept C over data set X with the only available information about C being its value over X. Our algorithm can guarantee that it best fits the training data. In other words:

"Any hypothesis found approximate the target function well over a sufficiently large set of training examples will also approximate the target function well over other unobserved examples."

For example, whether a person goes to a movie is based on four binary features with two values (true or false):

With the training data, we have two data objects as positive samples and one as negative:

Hypothesis Notations

Each of the data objects represents a concept and hypotheses. Considering a hypothesis <true, true, false, false> is more specific because it can cover only one sample. Generally, we can add some notations to this hypothesis. We have the following notations:

The hypothesis ⵁ will reject all the data samples. The hypothesis <? , ? , ? , ? > will accept all the data samples. The ? notation indicates that the values of this specific feature do not affect the result.

The total number of the possible hypothesis is (3 * 3 * 3 * 3) + 1 — 3 because one feature can have either true, false, or ? and one hypothesis for rejects all (ⵁ).

Q10. Write a short note on General to Specific concept

Solution: Many machine learning algorithms rely on the concept of the general-to-specific ordering of hypotheses.

Any instance classified by h1 will also be classified by h2. We can say that h2 is more general than h1. Using this concept, we can find a general hypothesis that can be defined over the entire dataset X.

To find a single hypothesis defined on X, we can use the concept of being more general than partial ordering. One way to do this is to start with the most specific hypothesis from H and generalize this hypothesis each time it fails to classify and observe positive training data object as positive.

If the next training sample is <true, true, false, false> and the current hypothesis is <ⵁ, ⵁ, ⵁ, ⵁ>, then we can directly replace our existing hypothesis with the new one.

If the next positive training sample is <true, true, false, true> and the current hypothesis is <true, true, false, false>, then we can perform a pairwise conjunctive. With the current hypothesis and next training sample, we can find a new hypothesis by putting ? in the place where the result of the conjunction is false:

<true, true, false, true> ⴷ <true, true, false, false> = <true, true, false, ?>

Now, we can replace our existing hypothesis with the new one: h <-<true, true, false, ?>

5. This step involves the repetition of Step 2 until we have more training samples.

6. Once there are no training samples, the current hypothesis is the one we wanted to find. We can use the final hypothesis to classify real objects.