CS607: Artificial Intelligence
CS607 Artificial Intelligence is a course taught at Virtual University of Pakistan. Practise every lecture of CS607 with lecture-wise quizzes and Past Papers Style Solved MCQs, then test yourself with midterm and final term mocks made from your own syllabus.
- 45 lectures
- Past papers style solved MCQs
- Answers in English and Roman Urdu
How to prepare for CS607
CS607 Lecture Quizzes
Each quiz covers a few lectures. New here? Start with Lecture 1-3 Quiz.
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CS607 Exam Mocks
A practice paper made only from the lectures in your syllabus. Pick the one that fits your next exam.
CS607 Leaderboard
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CS607 Artificial Intelligence
Gradup covers all 45 lectures of CS607 Artificial Intelligence with Past Papers Style Solved MCQs. Use the lecture quizzes while you study the CS607 handouts, then switch to the CS607 midterm and final term mocks. Every answer comes with an explanation and the handout reference, so you know exactly where to revise.
See all 45 CS607 lectures
- 1 CS607 Lecture 1Introduction
- 2 CS607 Lecture 2Formal Definitions for Artificial Intelligence
- 3 CS607 Lecture 3History and Evolution of Artificial Intelligence
- 4 CS607 Lecture 4Problem Solving
- 5 CS607 Lecture 5Problem Solving
- 6 CS607 Lecture 6Search Strategies
- 7 CS607 Lecture 7DFS, BFS and Progressive Deepening
- 8 CS607 Lecture 8Heuristically Informed Searches
- 9 CS607 Lecture 9Best First Search
- 10 CS607 Lecture 10Adversarial Search
- 11 CS607 Lecture 11Comment on Evolution
- 12 CS607 Lecture 12Genetic Algorithm Problems
- 13 CS607 Lecture 13Eight Queens Problem
- 14 CS607 Lecture 14Knowledge Representation and Reasoning
- 15 CS607 Lecture 15Formal KR techniques
- 16 CS607 Lecture 16Reasoning
- 17 CS607 Lecture 17Resolution
- 18 CS607 Lecture 18Expert Systems
- 19 CS607 Lecture 19Roles of an expert system
- 20 CS607 Lecture 20Expert System & Forward Chaining
- 21 CS607 Lecture 21Backward chaining
- 22 CS607 Lecture 22CLIPS
- 23 CS607 Lecture 23CLIPS Example
- 24 CS607 Lecture 24CLIPS Integrated Development Environment
- 25 CS607 Lecture 25Design of expert systems
- 26 CS607 Lecture 26Design of expert systems
- 27 CS607 Lecture 27Practical Approach
- 28 CS607 Lecture 28Handling uncertainty with fuzzy systems
- 29 CS607 Lecture 29Boolean versus Fuzzy
- 30 CS607 Lecture 30Fuzzy Set Representation
- 31 CS607 Lecture 31Fuzzy Inference System
- 32 CS607 Lecture 32Fuzzy Inference Process
- 33 CS607 Lecture 33Motivation
- 34 CS607 Lecture 34Machine Learning
- 35 CS607 Lecture 35Version Space and Searching
- 36 CS607 Lecture 36Candidate Elimination Algorithm
- 37 CS607 Lecture 37Decision Trees Learning
- 38 CS607 Lecture 38Information Gain
- 39 CS607 Lecture 39Connectionist
- 40 CS607 Lecture 40Multiple layers of Perceptrons
- 41 CS607 Lecture 41Supervised and Un-supervised Searches
- 42 CS607 Lecture 42Planning & Motivation
- 43 CS607 Lecture 43Computer Vision
- 44 CS607 Lecture 44Clustering
- 45 CS607 Lecture 45Conclusion
Frequently asked questions
Quick answers about practising CS607 on Gradup.
Gradup has lecture-wise CS607 quizzes with Past Papers Style Solved MCQs covering all 45 lectures, with explanations in English and Roman Urdu.
Yes. Choose the lectures that are in your midterm and Gradup builds a CS607 midterm mock from Past Papers Style Solved MCQs of only those lectures. Try again for other questions from the same lectures.
Yes. The CS607 final term mock mixes easy, medium and hard MCQs from the lectures you select, just like the real paper.
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