applications of linear algebra in field of IT.

1. ASSIGNMENT # 1
Submitted to:
Mam Hina Qurashi
Submitted by:
Rabia Zafar
17581556-045
Section ‘A’
BS IT
4th semester

3. Applications of linear algebra in field of IT
Linear algebra:
Linear algebra provides concepts that are crucial to many areas
of computer science, including graphics, image processing, cryptography,
machine learning, computer vision, optimization, graph algorithms,
quantum computation, computational biology, information retrieval and
web search.
Algebra is used in computerscience in many ways: Boolean algebra for
evaluating code paths, error correcting codes, processor optimization,
relational database design/optimization, and so forth.
1. Internet search
2. Network model
3. Machine learning
4. Graphs
5. Bioinformatics
6. Scientific computing
7. Data mining
8. Computer vision
9. Cryptography
10. Matrix computations
11. Parallel computing
Matrix computations:
Matrix computations are used in computer programming in many ways:
graphics, state-space modeling, arithmetic, ad hoc business logic, and so
forth.
Linear algebra as a sub-discipline is often taught in one of two ways: from a
computationalaspect of things, which focuses on matrices, their properties,
and operations on matrices; or, algebraically, where linear mappings are
treated as algebraic structures, and one studies, for instance, the group
theoretic relations that arise.In either case, you will not need to try too hard
to find situations where knowledge of either theoretical linear algebra or
matrix mathematics will be necessary.
Graphs:

4. Graph is very important part of computerscience. Graph theory is the
study of graphs which are mathematical structures used to model pairwise
relations between objects. A graph in this context is made up of
vectors, nodes, or points which are connected by edges, arcs, or lines. Linear
algebra is very important for solution of graphs in field of IT.
Machine learning:
The concepts of Linear Algebra are crucial for understanding thetheory
behind Machine Learning, especiallyfor Deep Learning. Theygive you better
intuition for how algorithms really work under the hood, which enables you
to make betterdecisions. Soif you really want to be a professionalin this
field, you cannot escape mastering some of its concepts. This post will give
you an introduction to the most important concepts of Linear Algebra that
are used in Machine Learning.
Network model:
For designing network models, linear algebra is also very helpful. We
required n no of hosts in a large network, n(n � 1)=2 (undirected) paths
between them, Want latency and packet loss rates for each path, Use info to
choose servers, route around faults. We make these models with the help of
linear algebra easily.
Cryptography:
Encryption and decryption require the use of some secret information,
usually refers to as a key. Example: let the message be, “PREPARE TO
NEGOTIATE”
WE ASSIGNA NUMBER FOR EACH LETTER OF ALPHABET.
Scientific computing:
Scientific computing is the science of solving problems with computers. The
problems themselves usually arise from other disciplines such as
mathematics, engineering, biology, physics, chemistryand other natural
sciences. As a consequence, scientific computing is interdisciplinary by
nature. Linear algebra help us to solve these type of problems.
END