2. Dijkstra's Algorithm
Dijkstra's algorithm allows us to find the shortest path between any two
vertices of a graph.
Follow the steps below to solve the problem:
Create a set sptSet (shortest path tree set) that keeps track of vertices included in the shortest-
path tree, i.e., whose minimum distance from the source is calculated and finalized. Initially, this
set is empty.
Assign a distance value to all vertices in the input graph. Initialize all distance values
as INFINITE. Assign the distance value as 0 for the source vertex so that it is picked first.
While sptSet doesn’t include all vertices
Pick a vertex u which is not there in sptSet and has a minimum distance value.
Include u to sptSet.
Then update distance value of all adjacent vertices of u.
To update the distance values, iterate through all adjacent vertices.
For every adjacent vertex v, if the sum of the distance value of u (from source) and
weight of edge u-v, is less than the distance value of v, then update the distance value
of v.
3.
4. What is A* Search Algorithm?
A* Search algorithm is one of the best and popular technique used in
path-finding and graph traversals.
OR
A computer method used to find the optimal path between two mapped
locations.
A* algorithm has 3 parameters:
g : the cost of moving from the initial cell to the current cell.
Basically, it is the sum of all the cells that have been visited since
leaving the first cell.
h : also known as the heuristic value, it is the estimated cost of
moving from the current cell to the final cell. The actual cost cannot
be calculated until the final cell is reached. Hence, h is the estimated
cost. We must make sure that there is never an over estimation of
the cost.
f : it is the sum of g and h. So, f = g + h
5.
6. Artificial Intelligence
Artificial intelligence is the simulation of human intelligence
processes by machines, especially computer systems.
Specific applications of AI include expert systems, natural
language processing, speech recognition and machine
vision.
7. What are the advantages and disadvantages of artificial intelligence?
Advantages
Good at detail-oriented jobs;
Reduced time for data-heavy tasks;
Delivers consistent results; and
AI-powered virtual agents are always available.
Disadvantages
Expensive
Requires deep technical expertise;
Limited supply of qualified workers to build AI tools;
Only knows what it's been shown; and
Lack of ability to generalize from one task to another.
8. CATEGORIES OF AI
Narrow AI -is when a machine has superior performance to a
human when doing one specific task
General AI – is when a machine is similar in its performance to a
human in any intellectual task
Strong AI – is when a machine has superior performance to a
human in many tasks
9.
10. What is machine learning?
Machine learning refers to the study of computer systems that learn and adapt automatically
from experience, without being explicitly programmed.
OR
A computer program that improves its performance at certain task with experience.
With simple AI, a programmer can tell a machine how to respond to various sets of instructions by
hand-coding each “decision.” With machine learning models, computer scientists can “train” a machine
by feeding it large amounts of data. The machine follows a set of rules—called an algorithm—to
analyze and draw inferences from the data. The more data the machine parses, the better it can
become at performing a task or making a decision.
Here’s one example you may be familiar with: Music streaming service Spotify learns your music
preferences to offer you new suggestions. Each time you indicate that you like a song by listening
through to the end or adding it to your library, the service updates its algorithms to feed you more
accurate recommendations. Netflix and Amazon use similar machine learning algorithms to offer
personalized recommendations.
11. What is Deep learning????
It is a machine learning technique that
teaches computers to do what comes
naturally to humans.
OR
In deep learning, a computer model learns to
perform classification tasks directly from
images, text, or sound.
13. In Supervised learning, the AI model is trained based on
the given input and its expected output, i.e., the label of the input.
The model creates a mapping equation based on the inputs and
outputs and predicts the label of the inputs in the future based on
that mapping equation.
Let’s suppose we have to develop a model that differentiates
between a cat and a dog. To train the model, we feed multiple
images of cats and dogs into the model with a label indicating
whether the image is of a cat or a dog. The model tries to develop
an equation between the input images and their labels. After
training, the model can predict whether an image is of a cat or a
dog even if the image is previously unseen by the model.
14. In Unsupervised learning, the AI model is trained
only on the inputs, without their labels. The model classifies
the input data into classes that have similar features. The
label of the input is then predicted in the future based on the
similarity of its features with one of the classes.
Suppose we have a collection of red and blue balls and we
have to classify them into two classes. Let’s say all other
features of the balls are the same except for their color. The
model tries to find the dissimilar features between the balls
on the basis of how the model can classify the balls into two
classes. After the balls are classified into two classes
depending on their color, we get two clusters of balls, one of
blue color and one of red color.
15. In Reinforcement learning, the AI model tries to take
the best possible action in a given situation to maximize the
total profit. The model learns by getting feedback on its past
outcomes.
Consider the example of a robot that is asked to choose a
path between A and B. In the beginning, the robot chooses
either of the paths as it has no past experience. The robot is
given feedback on the path it chooses and learns from this
feedback. The next time the robot gets into a similar
situation, it can use feedback to solve the problem. For
example, if the robot chooses path B and gets a reward, i.e.,
positive feedback, this time the robot knows that it has to
choose path B to maximize its reward.
16.
17. NOTE:
ML - A computer program that improves its performance at
certain task with experience.
DL – it structures algorithm in layers to create an artificial
neural network.
Neural networks - try to emulate the human brain, combining
computer science.
Graph theory - is the study of graphs, which are
mathematical structures used to model pairwise relations
between objects.
Trees - abstract data type with a hierarchical structure.
