Artificial Intelligence and Machine Learning

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

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