Academic year 2011/2012

Calendar Year May 2013 - Jan 2014

**Objectives :**To understand wide range of topics in compiler design.

**Syllabus :**

- Formal Languages : Language definition, grammers, finite state automata, regular expressions, lexical analysis.
- Syntax analysis : BNF, context-free grammars, recognisers, parse trees, top-down parsing, recursive descent, ambiguity, left recursion, backtracking, Warshall’s algorithm, context clashes, Bottom-up parsing, operator precedence grammars, constructing precedence matrices, producing abstract syntax trees and semantic actions, symbol and type tables, syntax directed translation.
- Storage Allocation : Run-time stack, heap, dope vectors, garbage collection
- Code Generation : Stack machines, assembly Language, P-Code, generating code for some typical constructs, machine code generation and optimisation.
- Error Diagnostics and recovery: Types of errors (lexical errors, syntax errors, type errors, runtime errors), detection of errors, recovery.
- Compiler construction tools : Yacc, Lex.

**Evaluation :**

In-Course Assessments : Two In-Course Assessments : An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation. (30%)

End of Course Examination: A written examination of three hours duration, where students will be required to answer four out of six questions (70%)

**Objectives :**

- To introduce basic concepts and techniques of Data Mining and Machine learning.
- To develop skills of using recent data mining and machine learning software for solving practical problems.

**Syllabus :**

- Introduction: Data Mining and Machine Learning, Data collection and ware house, Data cleaning and preparation for Knowledge Discovery, Knowledge Representation .
- Classification : Basic methods, Decision Trees, Rule-based methods.
- Neural networks : Introduction to neural networks, Multilayer neural networks, Error Back-propagation algorithm, RBF networks.
- Stochastic methods: Simulated annealing, Genetic algorithms, Genetic programming.
- Bayesian Learning : Bayesian decision theory (Continuous features, Discrete features), Bayesian belief networks.
- Clustering: Unsupervised Learning, K-means clustering, Unsupervised Bayesian learning, Kohonen networks.

**Evaluation :**

In-Course Assessments : Two In-Course Assessments : An In-Course Assessment may be either a written examination of half an hour duration or an assignment or a poster presentation or a Multimedia presentation. (30%)

End of Course Examination : A written examination of three hours duration, where students will be required to answer four out of six questions. (70%)

**Lecturers:** Dr. A. Ramanan and Dr. E.Y.A. Charles

**Reference Book:** Data Mining, Practical Machine Learning Tools and Techniques. By Ian H. Wiyyen and Eibe Frank - http://www.cs.waikato.ac.nz/ml/weka/book.html

**Software: **WEKA - http://www.cs.waikato.ac.nz/ml/weka/index.html

- Teacher: Dr. A. Ramanan

Advanced Algorithms - 4 credits

2-4 in-course exams: 30-60 miniutes each

an end of course exam: 03 hours