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.Line...