course outline

FAST—National University of Computer and Emerging Sciences

MS (Computer Science & Software Project Management)

CS 319 – Applied Programming- Fall 2012

Instructor: Rauf Ahemd Shams Malick ( raufmalick@gmail.com)

Assistant Professor (Adjunct Faculty),

Department of Computer Science

Date Topic

Week 1 Introduction to computer science, Graphs, Adjacency Matrix/Lists

Week 2 Applied problems and project sample details, Dynamic Programming technique and its

implementation techniques

Week 3-7 Lab #3,4,5, Project 1 (due in week 8th)

Week 8 Midterm Exam

Week 9 Introduction to programming frameworks

Week 10-13 Presentations, Code samples

Week 14 Future Challenges in programming

Week 15 Guest Speaker

Week 16 Final Exam

Learning and Ability Outcome

This course aims to develop advance programming skills within the MS computer science and project

management students. The course is divided into two parts first is ‘core programming’ i.e. before

midterm, the other is ‘higher level programming’ i.e. after midterm. The students will learn basic/new

challenges computer programming with the ability of writing core and higher level programs.

Course Material: Will be provided accordingly.

Marks Distribution

Midterm Examination 15

Labs Assignment. Quiz 15

Project 1 20

Project 2 20

Final Examination 30

New Problems and Programming Techniques

Project 1.

1. Computational Biology

a. Protein Sequence Alignments http://bix.ucsd.edu/bioalgorithms/book/excerpt-ch6.pdf

b. Protein Structural Alignments

c. Genome Sequence Mapping

2. Text Mining

a. Vector Space Modeling for Text Documents

b. Eigen Value Calculation

c. Sentiment Analysis

d. Word net http://wordnet.princeton.edu/wordnet/frequently-asked-questions/for-application-

developer/

e. Principal Component Analysis

f. Singular Value Decomposition

Reading Material

http://www.casact.org/pubs/forum/10spforum/francis_flynn.pdf

http://perso.uclouvain.be/vincent.blondel/publications/08-textmining.pdf

3. Voice Mining

a. Voice recognition

b. Voice matching in video

4. Graph Mining http://www.charuaggarwal.net/gtoc.pdf

a. Finding sub graphs

b. Handling huge graphs in memory

c. Visualization of huge graphs

d. Graph matching problems

5. Data Mining Algorithms

a. Spectral Clustering

b. Fuzzy Clustering

c. Nearest neighborhood algorithm

POST MIDTERM POJECT

1. Distributed Indexing (Hadoop, Map Reduce)

2. Use of machine learning library, NLP, Text Classification (Mallet)

3. Use of Distributed Indexing Server (LUCENE)

4. Application based on Struts, Spring, any MVC framework etc.

5. Data extraction from CRMs like Dynamics, Sales Force

6. Data extraction from existing Social Networking platforms like Facebook, LInkedin, etc…