Sem 4 and 6 Artificial Intelligence and Machine Learning Important Question CS3491 May/June 2026

Added: 2026-05-05

[00:00:05]Welcome students.

[00:00:08]May June 2026 Ana University exam subject code CS 3491 artificial intelligence and machine learning important questions.

[00:00:24]So first part questions what is AI? Why is it important?

[00:00:32]Write the applications of AI list out the problem solving agents.

[00:00:38]What is CSP?

[00:00:41]What is huristic search? What is backtracking? Unit one party questions.

[00:00:49]Next unit two party questions.

[00:00:52]Why does uncertainity arise?

[00:00:56]Write bias rule mention its use. What is causal network?

[00:01:02]Write the needs of probabilistic reasoning in AI. If P of A equal to 0.3 P of A on B equal to 0.4 P of B equal to 0.5, then P of B on A equal to what?

[00:01:16]What is the purpose of Basha network?

[00:01:21]important questions.

[00:01:27]Okay. What is least square? What is decision tree? Write its advantages and disadvantages.

[00:01:36]What are random forest? What is SPM? SPM another support vector mission. Compare simple regression and multiple regression. List out the linear classification models. Next unit four.

[00:01:54]What is voting? What is adab boost?

[00:01:58]Write its role. Define clustering. What is stacking? Compare bagging and boosting. Compare K means and KNN.

[00:02:07]Next unit five. Important party questions. What is perceptron?

[00:02:13]Compare single layer and multi-layer perceptron.

[00:02:17]Define gradient descent optimization.

[00:02:21]What is reel?

[00:02:24]Compare shallow and deep network. What are activation functions?

[00:02:31]Next part two important.

[00:02:37]Next part B. Okay. So part B important questions. twice. So first unit question number 11. What is constraint satisfaction problem?

[00:02:51]Cross plus road equal to danger.

[00:02:55]Map coloring problem. Okay. What is adversarial search? Explain minimax and alpha beta pruning with the example.

[00:03:05]Explain the following strategies.

[00:03:07]Uninformed search. Huristic search.

[00:03:11]Okay, next unit two kind of important part B question. Explain exact inference and approximate inference in Bashan network with example. Very very important. Explain the following causal networks Bashan inference.

[00:03:30]Next unit three kind of important part B question. Explain linear regression models in detail. What is maximum margin classifier? How support vector machine SPM is used in maximum margin classifier? Explain with the illustrations.

[00:03:49]Next unit four important part B question. What is instancebased learning?

[00:03:57]Explain KNN and its working with the example.

[00:04:02]What is unsupervised learning? Explain K means with the example.

[00:04:07]What are gshian mixture models? How expectations maximum algorithm is used to determine the likelihood estimates of parameters in probabilistic models.

[00:04:20]Next unit five important part B question explain the following vanishing gradient problem. Relu hyper marometer tuning batch normalization regularization dropout.

[00:04:39]Next explain stochastic gradient descent optimization.

[00:04:43]Explain error back propagation algorithm and discuss the shallow networks and deep networks in detail with the example.

[00:04:53]Next part C. Okay.

[00:04:58]Problems.

[00:04:59]So first theory question explain the working of decision tree random forest problems description problem based on navy bias theorem and regression. So part three very very important part.

[00:05:19]So in the paper so questions the balance question all the best. Next subject.

[00:05:58]Watch other episodes in the appropriate playlist. 4G silver academy strength success clarity.