Artificial Intelligence Tutorial

Introduction to Artificial Intelligence Intelligent Agents

Search Algorithms

Problem-solving Uninformed Search Informed Search Heuristic Functions Local Search Algorithms and Optimization Problems Hill Climbing search Differences in Artificial Intelligence Adversarial Search in Artificial Intelligence Minimax Strategy Alpha-beta Pruning Constraint Satisfaction Problems in Artificial Intelligence Cryptarithmetic Problem in Artificial Intelligence

Knowledge, Reasoning and Planning

Knowledge based agents in AI Knowledge Representation in AI The Wumpus world Propositional Logic Inference Rules in Propositional Logic Theory of First Order Logic Inference in First Order Logic Resolution method in AI Forward Chaining Backward Chaining Classical Planning

Uncertain Knowledge and Reasoning

Quantifying Uncertainty Probabilistic Reasoning Hidden Markov Models Dynamic Bayesian Networks Utility Functions in Artificial Intelligence


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Eight topics for research and thesis in AI

AI, abbreviated as Artificial Intelligence, is a field which has a long history. Artificial Intelligence is the ability of machines that perform the same function as human beings, like problem-solving, learning, reasoning and recognizing. The guiding principle of Artificial Intelligence is to concerning with human beings in reason, vision and speech. In today's world, the amount of data is generated by both machines and human beings. Recently Artificial Intelligence has created a lot of advantages for human beings. So much research is happening in all fields like computer vision, autonomous vehicles, the Internet of things (IoT), robotics and many more.

Machine learning

Machine learning is another branch of computer science and Artificial Intelligence whose primary work is to learn human results using data and algorithms. It has three types of machine learning algorithms; these are as follows:

  1. Supervised machine learning algorithm.
  2. Unsupervised machine learning algorithm.
  3. Reinforcement machine learning algorithm.

Deep learning

Deep learning is a part of Artificial Intelligence and Machine learning, whose primary function is to follow the human being that how they gain specific knowledge. This technology is used in driverless cars and makes them recognize road signs.

Reinforcement learning

In Machine learning, there is an area where all the rewards and punishments are given to desired and undesired behaviors, respectively. Various software and machines use reinforcement learning to find the best possible path that should operate in a specific situation. This learning algorithm method learns the action through the trial and error method. It means the algorithm knows the following step by understanding the current state. It is one of the best research projects and thesis for Artificial Intelligence.


Robotics is another branch of computer science and Artificial Intelligence. It is a field of Artificial Intelligence that deals with creating humanoid machines. These humanoid machines can behave like human beings and perform some actions. But only in certain situations, the robot can act like a human being. Artificial Intelligence comes from the use of robotics. Artificial Intelligence gives the power of thinking like a human being to the robot in all situations. Some robots can operate with the help of user input, whereas some other robots function autonomously.

Natural Language Processing

Natural language processing is a component of an Artificial Intelligence system which can understand the human language as it is spoken and written. It is also called natural language. Natural language processing enables the computer to understand human language. It has two main phases. These phases are as follows-

  1. Data preprocessing.
  2. Development of algorithm.

In 1st step, the text data is cleaned and prepared for machines to be analyzed. Once the preprocessing of data has been done, then the development of the algorithm takes place.

Computer vision

Computer vision is a subset of Artificial Intelligence in which machines can derive meaningful information from video, images and other visual inputs. Technically in computer vision, the retrieval of visual information is attempted by the machines. Computer vision uses a convolutional neural network (CNN) used to understand how pixels are different from each other. There are some uses of computer vision which are as follows-

  1. Biometric access management.
  2. Industrial robots and self-driving car
  3. Digital Diagnostic
  4. Augmented reality.

Recommender Systems

A recommender system is an information filtering technology which is commonly used on the e-commerce website and OTT platforms. The recommender system uses the habit, opinion and interest of the user; based on that; it provides a recommendation. A recommender system is based on content-based recommendations. In content-based recommendations, the content of all the items is analyzed. On the other hand, our past reading behavior is analyzed by collaborative filtering and based on that, and recommendation takes place.

Internet of Things

The Internet of things describes the network of the physical object, or IoT, which is embedded with sensors, software and other technology. Artificial Intelligence deals with the creation of a system which can learn to perform a human task without any input command. The Internet of things is also a network of various networks connected over the Internet. Now all the Internet of things devices collect a lot of data needs to be collected. This is where AI comes into use. The Artificial Intelligence algorithm uses these collected data.