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Default Sathyabama Institute of Science and Technology B.Tech IT SCSA1601 Machine learning Syllabus

Sathyabama Institute of Science and Technology B.Tech IT SCSA1601 Machine learning Syllabus

SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF COMPUTING

SCSA1601 MACHINE LEARNING
L T P Credits Total Marks
3 * 0 3 100

UNIT 1 INTRODUCTION TO MACHINE LEARNING 9 Hrs.
Machine learning - examples of machine learning applications - Learning associations - Classification -Regression -
Unsupervised learning - Supervised Learning - Learning class from examples - PAC learning -Noise, model
selection and generalization - Dimension of supervised machine learning algorithm.

UNIT 2 DECISION THEORY 9 Hrs.
Bayesian Decision Theory- Introduction- Classification - Discriminant function-Bayesian networks-Association rule -
Parametric Methods - Introduction - Estimation –Multivariate methods-Data Parameter estimation–Dimensionality
Reduction- PCA-Linear discriminant analysis.

UNIT 3 CLUSTERING AND REGRESSION 9
Hrs.
Clustering - Mixture densities - k-means clustering - Supervised Learning after clustering - Hierarchical clustering -
Nonparametric Methods - Density estimation - Generalization of multivariate data - Smoothing models -Decision Trees -
Univariate trees - Multivariate trees - Learning rules from data - Linear Discrimination-Gradient Descent.

UNIT 4 MULTILAYER PERCEPTRONS 9 Hrs.
Structure of brain - Neural networks as a parallel processing - Perceptron - Multilayer perceptron - Back propagation -
Training procedures - Tuning the network size - Learning time.

UNIT 5 LOCAL MODELS 9 Hrs.
Competitive learning - Adaptive resonance theory - Self organizing map -Radial Basis functions - Bagging- Boosting-
Reinforcement Learning.
Max. 45 Hrs.

COURSE OUTCOMES
On completion of the course, student will be able to
CO1 - Understand complexity of Machine Learning algorithms and their limitations.
CO2 - Understand modern notions in data analysis oriented computing.
CO3 - Be capable of confidently applying common Machine Learning algorithms in practice and implementing their own.
CO4 - Be capable of performing distributed computations
CO5 - Can demonstrate working knowledge of reasoning in the presence of incomplete and/or uncertain information.
CO6 - Gain ability to apply knowledge representation, reasoning, and machine learning techniques to real-world problems.

TEXT / REFERENCE BOOKS
1. Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, 2004.
2. Tom Mitchell, “Machine Learning”, McGraw Hill, 1997.
3. Shai Shalev-Shwartz and Shai Ben-David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge
University Press, 2014.

END SEMESTER EXAMINATION QUESTION PAPER PATTERN
Max. Marks: 100 Exam Duration: 3 Hrs.
PART A: 10 Questions carrying 2 marks each – No choice 20 Marks
PART B: 2 Questions from each unit of internal choice, each carrying 16 marks 80 Marks
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