Table of Contents
CS607 Handouts pdf
Course Category: Computer Science/Information Technology CS607 Handouts pdf
CS607: Artificial Intelligence Handouts (PDF)
Artificial intelligence (AI) is a broad branch of computer science that specializes in building intelligent machines capable of performing tasks that normally require human ingenuity. CS607 Handouts pdf
Course Outline
Introduction to Artificial Intelligence, Problem Solving, Genetic Algorithms, Knowledge Representation, and Reasoning, Expert Systems, Uncertainty (Introduction, Classical sets, Fuzzy sets, Fuzzy logic, Fuzzy inference system), Introduction to Learning (Symbol-based, Connectionist, Artificial Neural Networks supervised and unsupervised), Planning, Advanced Topics (Computer vision, Robotics, Soft-computing Clustering), Conclusion. CS607 Handouts pdf
Join VU assignment solution groups and also share with friends. In these WhatsApp groups, we send solution files, VU handouts, VU past papers, and links to you. To join WhatsApp groups click the below links.
ALSO, SEE:
FINAL TERM PAST PAPERS MEGA FILES
MUST JOIN VU STUDY GROUPS
CS607 HANDOUTS
CS607: Artificial Intelligence
Intelligence
Intelligence is one of the most talked about topics in psychology, but there is no general definition. However, it can often be defined as the ability to see or consider information and to store it as information to be applied to changing behaviors in a particular environment or context.
Artificial Intelligence (AI)
At its core, AI is a branch of computer science that aims to answer Turing’s question in a reassuring way. It is an attempt to duplicate or mimic human ingenuity in machines. Artificial intelligence (AI), is the ability of a digital computer or a computer-controlled robot to perform tasks that are commonly associated with intelligent creatures. Artificial intelligence (AI) is the intelligence manifested by machines, as opposed to the natural intelligence manifested by animals including humans.
AI research has been defined as the field of intelligent intelligence research, which refers to any system that recognizes its location and performs actions that increase its chances of achieving its goals. The term “artificial intelligence” was previously used to describe machines that mimic and demonstrate “human” cognitive abilities associated with the human mind, such as “learning” and “problem-solving”. This definition has since been rejected by major AI researchers who now define AI psychologically and functionally, which does not limit how intelligence can be defined.
AI Approaches
AI research follows two distinct, and to some extent competing, methods, the symbolic (or “top-down”) approach, and the connectionist (or “bottom-up”) approach. The top-down approach seeks to replicate intelligence by analyzing cognition independent of the biological structure of the brain, in terms of the processing of symbols whence the symbolic label. The bottom-up approach, on the other hand, involves creating artificial neural networks in imitation of the brain’s structure—whence the connectionist label.
Strong AI
The ultimate goal of strong AI is to produce a machine whose overall ingenuity is inseparable from humans. In the strong AI, there is no significant difference between a piece of software, namely AI, which closely mimics the actions of the human brain, and the actions of a person, which includes his or her cognitive abilities.
Applied AI
Applied AI, also known as advanced information processing, aims to produce useful “smart” systems — for example, “professional” medical diagnostic systems and stock trading systems. The AI used has enjoyed great success, as described in the professional systems section.
In Cognitive Simulation
In mind simulation, computers are used to test ideas about how the human brain works — for example, ideas about how people see faces or remember memories. A cognitive simulation is already a powerful tool in both neuroscience and psychology.
Neural Networks Reinvented
Although computer science had rejected the concept of neural networks after Minsky’s book and Papert’s Perceptrons, in the 1980s at least four different groups re-invented the background broadcasting learning algorithm first discovered in 1969 by Bryson and Ho. The algorithm used in many learning problems in computer science and the widespread distribution of results in the Collective Parallels Distributed Analysis (Rumelhart and McClelland, 1986) caused great excitement.
