lOMoAR cPSD| 40229986
CS34 91- ARTIFICIAL INTELLIGENCE
AND MACHINE LEARNING
QUESTION BANK
Downloaded by Meenakshi Dharmaraj ()
, lOMoAR cPSD| 40229986
CS3491 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
COURSE OBJECTIVES:
The main objectives of this course are to:
Study about uninformed and Heuristic search techniques.
Learn techniques for reasoning under uncertainty
Introduce Machine Learning and supervised learning algorithms
Study about ensembling and unsupervised learning algorithms
Learn the basics of deep learning using neural networks
UNIT I PROBLEM SOLVING
Introduction to AI - AI 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 – naïve bayes models. Probabilistic
reasoning – Bayesian networks – exact inference in BN – approximate inference in BN –
causal networks.
UNIT III SUPERVISED LEARNING
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 NETWORKS
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.
PRACTICAL EXERCISES:
1. Implementation of Uninformed search algorithms (BFS, DFS)
2. Implementation of Informed search algorithms (A*, memory-bounded A*)
3. Implement naïve Bayes models
4. Implement Bayesian Networks
5. Build Regression models
6. Build decision trees and random forests
7. Build SVM models
8. Implement ensembling techniques
9. Implement clustering algorithms
, lOMoAR cPSD| 40229986
10. Implement EM for Bayesian networks
11. Build simple NN models
12. Build deep learning NN models
COURSE OUTCOMES:
At the end of this course, the students will be able to:
CO1: Use appropriate search algorithms for problem solving
CO2: Apply reasoning under uncertainty
CO3: Build supervised learning models
CO4: Build ensembling and unsupervised models
CO5: Build deep learning neural network models
TEXT BOOKS:
1. Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, Fourth
Edition, Pearson Education, 2021.
2. Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Fourth Edition, 2020.
REFERENCES:
1. Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Pearson
Education,2007
2. Kevin Night, Elaine Rich, and Nair B., “Artificial Intelligence”, McGraw Hill, 2008
3. Patrick H. Winston, "Artificial Intelligence", Third Edition, Pearson Education, 2006
4. Deepak Khemani, “Artificial Intelligence”, Tata McGraw Hill Education, 2013
(http://nptel.ac.in/)
5. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
6. Tom Mitchell, “Machine Learning”, McGraw Hill, 3rd Edition,1997.
7. Charu C. Aggarwal, “Data Classification Algorithms and Applications”, CRC Press, 2014
8. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, “Foundations of Machine
Learning”, MIT Press, 2012.
9. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016
CS34 91- ARTIFICIAL INTELLIGENCE
AND MACHINE LEARNING
QUESTION BANK
Downloaded by Meenakshi Dharmaraj ()
, lOMoAR cPSD| 40229986
CS3491 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
COURSE OBJECTIVES:
The main objectives of this course are to:
Study about uninformed and Heuristic search techniques.
Learn techniques for reasoning under uncertainty
Introduce Machine Learning and supervised learning algorithms
Study about ensembling and unsupervised learning algorithms
Learn the basics of deep learning using neural networks
UNIT I PROBLEM SOLVING
Introduction to AI - AI 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 – naïve bayes models. Probabilistic
reasoning – Bayesian networks – exact inference in BN – approximate inference in BN –
causal networks.
UNIT III SUPERVISED LEARNING
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 NETWORKS
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.
PRACTICAL EXERCISES:
1. Implementation of Uninformed search algorithms (BFS, DFS)
2. Implementation of Informed search algorithms (A*, memory-bounded A*)
3. Implement naïve Bayes models
4. Implement Bayesian Networks
5. Build Regression models
6. Build decision trees and random forests
7. Build SVM models
8. Implement ensembling techniques
9. Implement clustering algorithms
, lOMoAR cPSD| 40229986
10. Implement EM for Bayesian networks
11. Build simple NN models
12. Build deep learning NN models
COURSE OUTCOMES:
At the end of this course, the students will be able to:
CO1: Use appropriate search algorithms for problem solving
CO2: Apply reasoning under uncertainty
CO3: Build supervised learning models
CO4: Build ensembling and unsupervised models
CO5: Build deep learning neural network models
TEXT BOOKS:
1. Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, Fourth
Edition, Pearson Education, 2021.
2. Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Fourth Edition, 2020.
REFERENCES:
1. Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Pearson
Education,2007
2. Kevin Night, Elaine Rich, and Nair B., “Artificial Intelligence”, McGraw Hill, 2008
3. Patrick H. Winston, "Artificial Intelligence", Third Edition, Pearson Education, 2006
4. Deepak Khemani, “Artificial Intelligence”, Tata McGraw Hill Education, 2013
(http://nptel.ac.in/)
5. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
6. Tom Mitchell, “Machine Learning”, McGraw Hill, 3rd Edition,1997.
7. Charu C. Aggarwal, “Data Classification Algorithms and Applications”, CRC Press, 2014
8. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, “Foundations of Machine
Learning”, MIT Press, 2012.
9. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016
