Understanding Machine Learning Techniques in Artificial Intelligence

24P-0539 Ahmad Moosa Sadiq — Sat, 14 Mar 2026 15:38:31 +0000

Link to the paper (https://www.researchgate.net/publication/382264507_A_Survey_of_Machine_Learning_Techniques_for_Artificial_Intelligence)

Machine Learning:

Machine learning allows systems to learn from data and improve their performance over time. Instead of programming a system with strict rules, machine learning algorithms analyze data to discover patterns and relationships.

Use cases:

1- Robotics

2- NLP

3- Finance

Types:

There are types of machine learning.

i)-Supervised learning

In this, the algorithms is trained with labeled data , we get output on the basis of paired input. Common models are SVM and linear regression.
Examples:

Email spam filtering

ii)-Unsupervised learning

In this, the algorithms is trained with unlabeled data , we use it when we unsure of the output. Common models are K-means.
Examples:

Customer segregation

iii)-Reinforcement Learning

In this, the algorithm learns itself from the data and the environment. Common models are PPO and DQN.
Examples:

Robotics

Comparative Analysis of Search Algorithms in AI

24P-0539 Ahmad Moosa Sadiq — Sat, 14 Mar 2026 15:22:31 +0000

Link to the paper(https://www.researchgate.net/publication/364921310_Comparative_Analysis_of_Search_Algorithms_in_AI)

Searching

Searching is a very important part of modern systems and there are many algorithms to achieve efficient searching.

Examples:

1- Navigation systems

2- Ai bots in games

3- Path finding

Types:

Search algorithms are divided into 2 categories.

i)-Uninformed Search

1- BFS

2- DFS

3- Iterative deepening search

4- UCS

5- Bidirectional search

6- Depth limited search

ii)-Informed Search

1- Best First

2- A*

3- Hill Climbing

Shortest Path:

Breadth First Seach is the algorithm which finds the shortest path by exploring each node level by level but it takes alot of memory as it explores each node and there are no weights to judge nodes.

Infinte Path Problem:

Depth limited search is used to get rid of infinite path problems by setting a max depth limit but if a goal is deeper than our set limit, this algorithm with fail to find the path.

Reduced Complexity:

Bidirectional search can reduce time complexity by starting frim initial and end goal to reach a middle node.

How heuristic values help in efficiency:

Algorithms which use heurtistic values reduce the search space and find the shortest and optimal path in no time.

DEV Community: 24P-0539 Ahmad Moosa Sadiq

Understanding Machine Learning Techniques in Artificial Intelligence

Link to the paper (https://www.researchgate.net/publication/382264507_A_Survey_of_Machine_Learning_Techniques_for_Artificial_Intelligence)

Machine Learning:

Types:

Comparative Analysis of Search Algorithms in AI

Link to the paper(https://www.researchgate.net/publication/364921310_Comparative_Analysis_of_Search_Algorithms_in_AI)

Searching

Types: