CS3491 artificial intelligence and machine learning important questions | Anna University

 CS3491 artificial intelligence and machine learning important questions

PART A

UNIT 1

AI Application

Artifical intelligence

agents and its types

ideal rational agent

environment and task environmrnt

UNIT 2

Approximate inference in BN/Bayes Rule

uncertainty and list the cause of uncertainty

Bayesian networks

Bayesian inference

Global and local semantics

casual networks

UNIT 3

Probabilistic Discriminative

Support vector machine

Bayesian Linear Regression

Single and Multiple Variables

Machine learning

Regression

UNIT 4

Bagging

Boosting

knn

voing

unsupervised learning

stacking

UNIT 5

stochastic gradient descenty

Perceptron

Regulrization

Multilayer perceptron

Error Backpropagation

PART B

UNIT 1

1.search strategies

• uninformed search strategies

• Heuristic search strategies

2. constraint satisfaction problems

3. Local search

4. adversarial search

unit 2

1.Bayesian networks 

2.causal networks

3.Bayesian inference

4.naïve bayes models

5.Bayes theorem and baye’s rules

unit 3

1.Linear Regression Models

•Least squares

•single & multiple variables

•gradient descent

2. Maximum margin classifier

•Support vector machine

3. Decision Tree, Random forests, Logistic regression

4. Discriminant function

5. Probabilistic discriminative model

unit 4

1.Ensemble Learning - bagging, boosting, stacking

2.K-means, Instance Based Learning

3.KNN

4.Voting

5.Model combination schemes

unit 5

1.Regularization

2.hyperparameter tuning

3.ReLU

4.error backpropagation,

5.activation functions

6.Perceptron