course outline
TRANSCRIPT
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 ( [email protected])
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…