CS3491-ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

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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.  na├»ve 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

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Contact uS for more updates *These questions are expected for the exams This may or may not be asked for exams
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SYllabuS

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.

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