This document discusses artificial intelligence (AI) and its goals of creating expert systems and implementing human intelligence in machines. It contributes to various fields including computer science, biology, philosophy, psychology, neuroscience, and math. The document also discusses types of intelligence, research areas in AI, types of learning including auditory, episodic, motor, observational, relational, spatial, perceptual, stimulus-response, and learning in AI including supervised, unsupervised, and reinforcement learning. It provides examples of supervised learning algorithms and unsupervised learning algorithms. Finally, it discusses artificial neural networks, including neurons, neural networks, types of neural networks such as feedforward and feedback networks, and applications of AI such as natural language processing.

1. ARTIFICIAL
INTELLIGENCE

3. GOAL OF AITO CREATE EXPERT
SYSTEMS
TO IMPLEMENT HUMAN
INTELLIGENCE IN
MACHINES

4. CONTRIBUTE TO AI
COMPUTER
SCIENCE BIOLOGY
PHILOSOPH
Y
PSYCHOLOG
Y
NEUROSCIEN
CE
MATHS

5. WHAT IS AN INTELLIGENCE ?
LINGUISTIC MUSICAL
SPATIAL
LOGICAL AND
MATHEMATICAL
INTELLIGENCE
INTELLIGENC
E
BODILY-
KINESTHETIC
INTRA PERSONAL
INTER
PERSONAL

6. RESEARCH AREAS OF ARTIFICIAL
INTELLIGENCE
ARTIFICI
AL
INTELLIG
ENCE
FUZZY
LOGIC
NATURAL
LANGUAGE
PROCESSIN
G
NEURAL
NETWORK ROBOTICS
EXPERT
SYSTEMS

7. TYPES OF LEARNIING
AUDIOTRY LEARNING :

8. EPISODIC LEARNING

9. MOTOR LEARNING

10. OBSERVATION LEARNING

11. RELATIONAL LEARNING

12. SPATIAL LEARNING

13. PERCEPTUAL LEARNING

14. STIMULUS –RESPONSE LEARNING

15. LEARNINGS IN AI
supervised
NON
SUPERVISED
REINFORCEMENT

16. SUPERVISED LEARNING

18. SUPERVISED LEARNING
• Supervised Learning Algorithms:
• All classification and regression algorithms come under supervised learning.
• Logistic Regression
• Decision trees
• Support vector machine (SVM)
• k-Nearest Neighbors
• Naive Bayes
• Random forest
• Linear regression
• polynomial regression
• SVM for regression

19. UNSUPERVISED LEARNING

21. UNSUPERVISED
• Unsupervised learning algorithms:
• All clustering algorithms come under unsupervised learning
algorithms.
• K – means clustering
• Hierarchical clustering
• Hidden Markov models

22. REINFORCEMENT LEARNING
• This type of Machine Learning algorithms allows software agents and
machines to automatically determine the ideal behaviour within a
specific context, to maximise its performance.

23. ANN(ARITIFICIAL NEURAL NETWORKS
• HUMAN BRAIN

24. NEURON

25. NEURAL NETWORKS
• An Artificial Neural Network (ANN) is an information processing
paradigm that is inspired by the way biological nervous systems, such
as the brain, process information.
• The key element of this paradigm is the novel structure of the
information processing system. It is composed of a large number of
highly interconnected processing elements (neurons) working in unison
to solve specific problems. ANNs, like people, learn by example.
• An ANN is configured for a specific application, such as pattern
recognition or data classification, through a learning process. Learning
in biological systems involves adjustments to the synaptic connections
that exist between the neurons

27. TYPES OF NEURAL NETWORKS
• FeedForward ANN
• The information flow is unidirectional. A unit sends information to other
unit from which it does not receive any information. There are no
feedback loops. They are used in pattern
generation/recognition/classification. They have fixed inputs and
outputs.

29. FEEDBACK ANN
• feedback loops are allowed. They are used in content addressable
memories

30. APPLICATION OF AI
• NATUARL LANGUAGE PROCESSING