Stuff Sector
Prepared by
S.Santhosh (Admin)
Important questions
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** Most important question
UNIT I
1. Risks and Benefits of A, Problem Solving Agents
Foundations of Al-History of Al
2. Problems Uninformed Search Breadth First Search Dijkstra's algorithm or uniform-cost search,Depth First Search Depth Limited Search *****
UNIT II
1. Informed Search Greedy Best First A algorithm Adversarial Game and Search Game theory Optimal decisions in game-Min Max Search algorithm
2. (CSP), Job Scheduling Backtracking Search for CSP LEARNING***
UNIT III
Machine Learning:
1 . Training and test sets, cross validation, Concept of over fitting, under fitting, Bias and Variance
2. Regression approaches of machine learning models Types of learning
3.Linear Regression ,Logistic Regression
UNIT IV
SUPERVISED LEARNING
1. Entropy Information gain Gini Impurity classification algorithm Rule based Classification
2.Naïve Bayesian classification, (SVM) **
UNIT V
1. Unsupervised Learning Principle Component Analysis Competitive Nets Kohonen Self
2.Types of Clustering**
3.Hierarchical clustering algorithms-k-means algorithm***
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**Very important questions are bolded and may be asked based on this topic
PART-C
1.Compulsory Questions {a case study where the student will have to read and analyse the subject }mostly asked from unit 2, 5(OR) a situation given and you have to answer on your own
** Most important question
UNIT I
1. Risks and Benefits of A, Problem Solving Agents
Foundations of Al-History of Al
2. Problems Uninformed Search Breadth First Search Dijkstra's algorithm or uniform-cost search,Depth First Search Depth Limited Search *****
UNIT II
1. Informed Search Greedy Best First A algorithm Adversarial Game and Search Game theory Optimal decisions in game-Min Max Search algorithm
2. (CSP), Job Scheduling Backtracking Search for CSP LEARNING***
UNIT III
Machine Learning:
1 . Training and test sets, cross validation, Concept of over fitting, under fitting, Bias and Variance
2. Regression approaches of machine learning models Types of learning
3.Linear Regression ,Logistic Regression
UNIT IV
SUPERVISED LEARNING
1. Entropy Information gain Gini Impurity classification algorithm Rule based Classification
2.Naïve Bayesian classification, (SVM) **
UNIT V
1. Unsupervised Learning Principle Component Analysis Competitive Nets Kohonen Self
2.Types of Clustering**
3.Hierarchical clustering algorithms-k-means algorithm***
Don't share as screenshot -Stuff sector
**Very important questions are bolded and may be asked based on this topic
don't waste my hardwork and valuable time
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*These questions are expected for the exams This may or may not be asked for exams All the best.... from admin Santhosh
Thanks for your love and support guys keep supporting and share let the Engineers know about Us and leave a comment below for better improvements If there is any doubt feel free to ask me I will clear if I can or-else I will say some solutions ..get me through WhatsApp for instant updates ~$tuff$£ctorSYllabuSUNIT I
INTELLIGENT AGENTS
Introduction to Al - Agents and Environments - concept of rationality - nature of environments structure of agents. Problem solving agents - search algorithms - uninformed search strategies.
UNIT II
PROBLEM SOLVING
Heuristic search strategies - heuristic functions. Local search and optimization problems - local search in continuous space - search with non-deterministic actions - search in partially observable environments - online search agents and unknown environments
UNIT III
GAME PLAYING AND SP
Game theory - optimal decisions in games - alpha-beta search - monte-carlo tree search stochastic games - partially observable games. Constraint satisfaction problems - constraint propagation - backtracking search for CSP - local search for CSP - structure of CSP.
UNIT IV
LOGICAL REASONING
Knowledge-based agents - propositional logic - propositional theorem proving - propositional model checking - agents based on propositional logic. First-order logic - syntax and semantics - knowledge representation and engineering - inferences in first-order logic - forward chaining backward chaining - resolution.
UNIT V
PROBABILISTIC REASONING
Acting under uncertainty - Bayesian inference - naive Bayes models. Probabilistic reasoning
Bayesian networks - exact inference in B - approximate inference in BN - causal networks.
UNIT I
INTELLIGENT AGENTS
Introduction to Al - Agents and Environments - concept of rationality - nature of environments structure of agents. Problem solving agents - search algorithms - uninformed search strategies.
UNIT II
PROBLEM SOLVING
Heuristic search strategies - heuristic functions. Local search and optimization problems - local search in continuous space - search with non-deterministic actions - search in partially observable environments - online search agents and unknown environments
UNIT III
GAME PLAYING AND SP
Game theory - optimal decisions in games - alpha-beta search - monte-carlo tree search stochastic games - partially observable games. Constraint satisfaction problems - constraint propagation - backtracking search for CSP - local search for CSP - structure of CSP.
UNIT IV
LOGICAL REASONING
Knowledge-based agents - propositional logic - propositional theorem proving - propositional model checking - agents based on propositional logic. First-order logic - syntax and semantics - knowledge representation and engineering - inferences in first-order logic - forward chaining backward chaining - resolution.
UNIT V
PROBABILISTIC REASONING
Acting under uncertainty - Bayesian inference - naive Bayes models. Probabilistic reasoning
Bayesian networks - exact inference in B - approximate inference in BN - causal networks.