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S.Santhosh (Admin)
Important questions
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** Most important question
UNIT I
1. Al Applications search strategies
2. (CSP) Problem solving agents search algorithms uninformed Local search and optimization problems **
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UNIT II
1. naïve bayes models**
2. Bayesian inference***
3. BN-approximate inference in BN
** Most important question
UNIT I
1. Al Applications search strategies
2. (CSP) Problem solving agents search algorithms uninformed Local search and optimization problems **
Don't share as screenshot -Stuff sectorUNIT II
1. naïve bayes models**
2. Bayesian inference***
3. BN-approximate inference in BN
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UNIT III
1.Least squares, single & multiple variables, Bayesian linear regression ***
2.Naive Bayes, Maximum margin classifier Support 3.vector machine, Decision Tree**
UNIT IV
1. Gaussian mixture models and Expectation maximization ***
2.bagging,boosting, stacking,(k means **)
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UNIT V
1.Activation functions, network training gradient descent optimization
2.Unit saturation (aka the vanishing gradient problem) ReLU***
3.hyperparameter tuning. batch normalization, regularization**
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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
**Very important questions are bolded and may be asked based on this topic
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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
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UNIT I PROBLEM SOLVINg
Introduction to Al Al Applications Problem solving agents search algorithms - uninformed search strategies Heuristic search strategies Local search and optimization problems adversarial search-constraint satisfaction problems (CSP)
UNIT II PROBABILISTIC REASONING
Acting under uncertainty Bayesian inference nalve bayes models. Probabilistic reasoning Bayesian networks-exact inference in BN-approximate inference in BN-causal networks.
UNIT III SUPERVISED LEARNIN
Introduction to machine learning Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function-Probabilistic discriminative model-Logistic regression, Probabilistic generative model - Naive Bayes, Maximum margin classifier-Support vector machine, Decision Tree, Random forests
UNIT IV ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING
Combining multiple learners: Model combination schemes, Voting. Ensemble Learning - bagging, boosting, stacking. Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization
UNIT V NEURAL NETWORK
Perceptron Multilayer perceptron, activation functions, network training gradient descent optimization stochastic gradient descent, error backpropagation, from shallow networks to deep networks-Unit saturation (aka the vanishing gradient problem) - ReLU, hyperparameter tuning. batch normalization, regularization, dropout.
UNIT I
UNIT I PROBLEM SOLVINg
Introduction to Al Al Applications Problem solving agents search algorithms - uninformed search strategies Heuristic search strategies Local search and optimization problems adversarial search-constraint satisfaction problems (CSP)
UNIT II PROBABILISTIC REASONING
Acting under uncertainty Bayesian inference nalve bayes models. Probabilistic reasoning Bayesian networks-exact inference in BN-approximate inference in BN-causal networks.
UNIT III SUPERVISED LEARNIN
Introduction to machine learning Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models: Discriminant function-Probabilistic discriminative model-Logistic regression, Probabilistic generative model - Naive Bayes, Maximum margin classifier-Support vector machine, Decision Tree, Random forests
UNIT IV ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING
Combining multiple learners: Model combination schemes, Voting. Ensemble Learning - bagging, boosting, stacking. Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization
UNIT V NEURAL NETWORK
Perceptron Multilayer perceptron, activation functions, network training gradient descent optimization stochastic gradient descent, error backpropagation, from shallow networks to deep networks-Unit saturation (aka the vanishing gradient problem) - ReLU, hyperparameter tuning. batch normalization, regularization, dropout.