Lab works based CSE 4459.
Course Catalogue
Course Code: CSE 4460
Course Name:
Big Data Analytics Lab
Credit Hours:
1.00
Detailed Syllabus:
Course Code: CSE 4461
Course Name:
Digital Image Processing
Credit Hours:
3.00
Detailed Syllabus:
Introduction to image processing: Image processing applications, image processing goals, image function, image representation, sampling and quantization, gray scale, binary (black and white), and color images, histograms, noise in images. Color image models: RGB, HIS, YIQ models. Image enhancement, convolution and filtering: Point processing, histogram equalization, histogram modeling, and histogram specification, spatial filtering – image smoothing, median filtering Edge detections: Sobel, Prewit, Laplacian and Canny edge detectors Image segmentation: Thresholding Shape detection, image matching and texture: image moments, central moments, moment invariants, template matching, area correlation, texture description, Image morphology: Basic morphological concepts, structuring elements, erosion, dilation, thinning, thickening, opening, and closing operations.
Course Code: CSE 4462
Course Name:
Digital Image Processing Lab
Prerequisite:
Credit Hours:
1.00
Detailed Syllabus:
Lab works based CSE 4461.
Course Code: CSE 4463
Course Name:
Introduction to Bioinformatics
Credit Hours:
3.00
Detailed Syllabus:
Introduction; Molecular biology basics: DNA, RNA, genes, and proteins; Graph algorithms: DNA sequencing, DNA fragment assembly, Spectrum graphs; Sequence similarity; Suffix Tree and variants with applications; Genome Alignment: maximum unique match, LCS, mutation sensitive alignments; Database search: Smith-Waterman algorithm, FASTA, BLAST and its variations; Locality sensitive hashing; Multiple sequence alignment; Phylogeny reconstruction; Phylogeny comparison: similarity and dissimilarity measurements, consensus tree problem; Genome rearrangement: types of genome rearrangements, sorting by reversal and other operations; Motif finding; RNA secondary structure prediction; Peptide sequencing; Population genetics; Recent Trends in Bioinformatics.
Course Code: CSE 4463
Course Name:
Introduction to Bioinformatics
Credit Hours:
3.00
Detailed Syllabus:
Introduction; Molecular biology basics: DNA, RNA, genes, and proteins; Graph algorithms: DNA sequencing, DNA fragment assembly, Spectrum graphs; Sequence similarity; Suffix Tree and variants with applications; Genome Alignment: maximum unique match, LCS, mutation sensitive alignments; Database search: Smith-Waterman algorithm, FASTA, BLAST and its variations; Locality sensitive hashing; Multiple sequence alignment; Phylogeny reconstruction; Phylogeny comparison: similarity and dissimilarity measurements, consensus tree problem; Genome rearrangement: types of genome rearrangements, sorting by reversal and other operations; Motif finding; RNA secondary structure prediction; Peptide sequencing; Population genetics; Recent Trends in Bioinformatics.
Course Code: CSE 4465
Course Name:
Natural Language Processing
Credit Hours:
3.00
Detailed Syllabus:
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. In this course, you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications. We will also discuss deep reinforcement learning techniques applied in NLP and Vision-Language Multimodal Intelligence.
Course Code: CSE 4465
Course Name:
Natural Language Processing
Credit Hours:
3.00
Detailed Syllabus:
Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. In this course, you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications. We will also discuss deep reinforcement learning techniques applied in NLP and Vision-Language Multimodal Intelligence.
Course Code: CSE 4467
Course Name:
Topics of Current Interest
Credit Hours:
3.00
Detailed Syllabus:
As necessary.
Course Code: CSE 4467
Course Name:
Topics of Current Interest
Credit Hours:
3.00
Detailed Syllabus:
As necessary.
Course Code: CSE 4469
Course Name:
Software Requirements Specification and Analysis
Credit Hours:
3.00
Detailed Syllabus:
This course offers to develop effective functional and non-functional requirements that are complete, concise, correct, consistent, testable and unambiguous, select the appropriate requirements elicitation techniques to identify requirements, design a set of software models to be used to flesh out hidden requirements and drive clarity into the system functional requirements, effectively analyze requirements and prioritize accordingly, perform requirements engineering in the context of the most common software development life cycles and processes, create a requirements specification to communicate requirements to a broad set of stakeholders, utilize various requirements validation techniques to critically evaluate their requirements to identify defects, manage change to requirements.