Page 93 - B.E CSE Curriculum and Syllabus R2017 - REC

Department of CSE, REC

OUTCOMES:
On successful completion of this course, the student will be able to:
● Familiar with the architecture of multicore architecture.
● Understand the challenges and design parallel programs.
● Develop shared memory programs using OpenMP.
● Develop distributed memory programs using MPI.
● Program Parallel Processors.

TEXT BOOKS:
1. Peter S. Pacheco, An Introduction to Parallel Programming, Morgan-Kauffman/Elsevier, First
Edition, 2011.
2. Darryl Gove, Multicore Application Programming for Windows, Linux, and Oracle Solaris, Pearson,
First Edition, 2011.

REFERENCES:
1. Michael J Quinn, Parallel programming in C with MPI and OpenMP, Tata McGraw Hill, First
Edition, 2003.
2. Shameem Akhter and Jason Roberts, Multi-core Programming, Intel Press, First Edition, 2006.

CS17E74 MACHINE LEARNING TECHNIQUES L T P C
3 0 0 3
OBJECTIVES:
● To have a thorough understanding of the Supervised and Unsupervised learning techniques.
● To know the basic concepts of neural networks.
● To study the various probabilities based learning techniques.
● To familiarize the basic concepts of genetic algorithms.
● To understand graphical models of machine learning algorithms.

UNIT I INTRODUCTION 9
Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron – Design a
Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept
Learning as Search – Finding a Maximally Specific Hypothesis – Version Spaces and the Candidate
Elimination.

UNIT II LINEAR MODELS 9
Multi-layer Perceptron – Going Forwards – Going Backwards: Back Propagation Error – Multi-layer
Perceptron in Practice – Examples of using the MLP – Overview – Deriving Back-Propagation – Radial Basis
Functions – Concepts – RBF Network –– Support Vector Machines - Regression Modeling.

UNIT III TREE AND PROBABILISTIC MODELS 9
Learning with Trees – Decision Trees – Constructing Decision Trees – Classification and Regression Trees –
Ensemble Learning – Boosting – Bagging – Different ways to Combine Classifiers – Probability and
Learning: Data into Probabilities – Basic Statistics – Gaussian Mixture Models – Nearest Neighbor Methods
– Unsupervised Learning – K means Algorithms – Vector Quantization.

UNIT IV DIMENSIONALITY REDUCTION AND EVOLUTIONARY MODELS 9
Dimensionality Reduction – Linear Discriminant Analysis – Principal Component Analysis – Factor Analysis
– Independent Component Analysis – Locally Linear Embedding – Isomap – Least Squares Optimization –
Evolutionary Learning – Genetic algorithms – Genetic Offspring: - Genetic Operators – Using Genetic
Algorithms – Reinforcement Learning – Overview – Getting Lost Example.

Curriculum and Syllabus | B.E. Computer Science and Engineering | R2017 Page 93