SS-1101T: Ideology and Constitution of Pakistan (Cr Hr 2+0)

(Prerequisite: none)

Historical background of Pakistan: Muslim society in Indo-Pakistan, the movement led by the societies, the downfall of Islamic society, the establishment of British Raj- Causes and consequences. Political evolution of Muslims in the twentieth century: Sir Syed Ahmed Khan; Muslim League; Nehru; Allama Iqbal: Independence Movement; Lahore Resolution; Pakistan culture and society, Constitutional and Administrative issues, Pakistan and its geopolitical dimension, Pakistan and International Affairs, Pakistan and the challenges ahead.

CS-1101T: Programming Fundamentals (Cr Hr 3+1)

(Prerequisite: none)

Introduction to problem solving, a brief review of Von-Neumann architecture, Introduction to programming, role of compiler and linker, introduction to algorithms, basic data types and variables, input/output constructs, arithmetic, comparison and logical operators, conditional statements and execution flow for conditional statements, repetitive statements and execution flow for repetitive statements, lists and their memory organization, multi-dimensional lists, introduction to modular programming, function definition and calling, stack rolling and unrolling, string and string operations, pointers/references, static and dynamic memory allocation, File I/O operations.

MT-1101T: Linear Algebra (Cr Hr 3+0)

(Prerequisite: none)

Algebra of linear transformations and matrices. determinants, rank, systems of equations, vector spaces, orthogonal transformations, linear dependence, linear Independence and bases, eigenvalues and eigenvectors, characteristic equations, Inner product space and quadratic forms.

NS-1101T: Applied Physics (Cr Hr 3+0)

(Prerequisite: none)

Electric force and its applications and related problems, conservation of charge, charge quantization, Electric fields due to point charge and lines of force. Ring of charge, Disk of charge, A point charge in an electric field, Dipole in a n electric field, The flux of vector field, The flux of electric field, Gauss’ Law, Application of Gauss’ Law, Spherically symmetric charge distribution, A charge isolated conductor, Electric potential energy, Electric potentials, Calculating the potential from the field and related problem Potential due to point and continuous charge distribution, Potential due to dipole, equipotential surfaces, Calculating the field from the potential , Electric current, Current density, Resistance, Resistivity and conductivity, Ohm’s law and its applications, The Hall effect, The magnetic force on a current, The Biot- Savart law, Line of B, Two parallel conductors, Amperes’ s Law, Solenoid, Toroids,

Faraday’s experiments, Faraday’s Law of Induction, Lenz’s law, Motional emf, Induced electric field, Induced electric fields, The basic equation of electromagnetism, Induced Magnetic field, The displacement current, Reflection and Refraction of light waves, Total internal reflection, Two source interference, Double Slit interference, related problems, Interference from thin films, Diffraction and the wave theory, related problems, Single-Slit Diffraction, related problems, Polarization of electromagnetic waves, Polarizing sheets, related problems.

CS-1102T: Application of Information & Communication Technologies

(Cr Hr 2+1)

(Prerequisite: none)

Brief history of Computer, Four Stages of History, Computer Elements, Processor, Memory, Hardware, Software, Application Software its uses and Limitations, System Software its Importance and its Types, Types of Computer (Super, Mainframe, Mini and Micro Computer), Introduction to CBIS (Computer Based Information System), Methods of Input and Processing, Class2. Organizing Computer Facility, Centralized Computing Facility, Distributed Computing Facility, Decentralized Computing Facility, Input Devices. Keyboard and its Types, Terminal (Dump, Smart, Intelligent), Dedicated Data Entry, SDA (Source Data Automation), Pointing Devices, Voice Input, Output Devices. Soft- Hard Copies, Monitors and its Types, Printers and its Types, Plotters, Computer Virus and its Forms, Storage Units, Primary and Secondary Memories, RAM and its Types, Cache, Hard Disks, Working of Hard Disk, Diskettes, RAID, Optical Disk Storages (DVD, CD ROM), Magnetic Types, Backup System, Data Communications, Data Communication Model, Data Transmission, Digital and Analog Transmission, Modems, Asynchronous and Synchronous Transmission, Simplex. Half Duplex, Full Duplex Transmission, Communications, Medias (Cables, Wireless), Protocols, Network Topologies (Star, Bus, Ring), LAN, LAN, Internet, A Brief History, Birthplace of ARPA Net, Web Link, Browser, Internet Services provider and Online Services Providers, Function and Features of Browser, Search Engines, Some Common Services available on Internet.

SS-1102T: Islamic Studies (Cr Hr 2+0)

(Prerequisite: none)

Basic Themes of Quran, Introduction to Sciences of Hadith, Introduction to Islamic Jurisprudence, Primary & Secondary Sources of Islamic Law, Makken & Madnian life of the Prophet, Islamic Economic System, Political theories, Social System of Islam. Definition of Akhlaq.The Most Important Characters mentioned in the Holy Qur’an and Sunnah, SIDQ (Truthfulness)Generosity Tawakkaul(trust on Allah)Patience Taqua (piety). Haqooq ul ibad in the light of Quran & Hadith – the important characteristic of Islamic Society.

SS-1103T: Ethical Behavior (Cr Hr 2+0)

(Prerequisite: none)

Scope and methods of Ethics: Ethics and religion; Ethical teachings of world religions; Basic moral concepts, right and wrong, good and evil; Outline of ethical systems in philosophy; Hedonism, utilitarianism, rationalism, self realization theories, Intuitionism; Islamic moral theory: Ethics of Quran and its philosophical basis, ethical percepts of Quran and Hadith and promotion of moral values in society.

MT-1202T: Calculus and Analytical Geometry (Cr Hr 3+0)

(Prerequisite: none)

Limits and Continuity; Introduction to functions, Introduction to limits, Techniques of funding limits, Indeterminate forms of limits, Continuous and discontinuous functions and their applications, Differential calculus; Concept and idea of differentiation, Geometrical and Physical meaning of derivatives, Rules of differentiation, Techniques of differentiation, Rates of change, Tangents and Normals lines, Chain rule, implicit differentiation, linear approximation, Applications of differentiation; Extreme value functions, Mean value theorems, Maxima and Minima of a function for single-variable, Concavity, Integral calculus; Concept and idea of Integration, Indefinite Integrals, Techniques of integration, Riemann sums and Definite Integrals, Applications of definite integrals, Improper integral, Applications of Integration; Area under the curve, Analytical Geometry; Straight lines in R3, Equations for planes.

SS-1204T: Functional English (Cr Hr 3+0)

(Prerequisite: none)

Paragraph and Essay Writing, Descriptive Essays; Sentence Errors, Persuasive Writing; How

to give presentations, Sentence Errors; Oral Presentations, Comparison and Contrast Essays,

Dialogue Writing, Short Story Writing, Review Writing, Narrative Essays, Letter Writing.

CS-1203T: Object Oriented Programming (Cr Hr 3+1)

(Prerequisite: CS-1101T)

Introduction to object oriented design, history and advantages of object oriented design, introduction to object oriented programming concepts, classes, objects, data encapsulation, constructors, destructors, access modifiers, const vs non-const functions, static data members & functions, function overloading, operator overloading, identification of classes and their relationships, composition, aggregation, inheritance, multiple inheritance, polymorphism, abstract classes and interfaces, generic programming concepts, function & class templates, standard template library, object streams, data and object serialization using object streams, exception handling.

EE-1201T: Digital Logic Design (Cr Hr 3+1)

(Prerequisite: none)

Number Systems, Logic Gates, Boolean Algebra, Combination logic circuits and designs, Simplification Methods (K-Map, Quinn Mc-Cluskey method), Flip Flops and Latches, Asynchronous and Synchronous circuits, Counters, Shift Registers, Counters, Triggered devices & its types. Binary Arithmetic and Arithmetic Circuits, Memory Elements, State Machines. Introduction Programmable Logic Devices (CPLD, FPGA); Lab Assignments using tools such as Verilog HDL/VHDL, MultiSim.

CS-2104T: Data Structure & Algorithms (Cr Hr 3+1)

(Prerequisite: CS-1101T)

Abstract data types, complexity analysis, Big Oh notation, Stacks (linked lists and array implementations), Recursion and analyzing recursive algorithms, divide and conquer algorithms, Sorting algorithms (selection, insertion, merge, quick, bubble, heap, shell, radix, bucket), queue, dequeuer, priority queues (linked and array implementations of queues), linked list & its various types, sorted linked list, searching an unsorted array, binary search for sorted arrays, hashing and indexing, open addressing and chaining, trees and tree traversals, binary search trees, heaps, M-way tress, balanced trees, graphs, breadth-first and depth-first traversal, topological order, shortest path, adjacency matrix and adjacency list implementations, memory management and garbage collection.

CS-2105T: Discrete Structures (Cr Hr 3+0)

(Prerequisite: none)

Mathematical reasoning, propositional and predicate logic, rules of inference, proof by induction, proof by contraposition, proof by contradiction, proof by implication, set theory, relations, equivalence relations and partitions, partial orderings, recurrence relations, functions, mappings, function composition, inverse functions, recursive functions, Number Theory, sequences, series, counting, inclusion and exclusion principle, pigeonhole principle, permutations and combinations, elements of graph theory, planar graphs, graph coloring, euler graph, Hamiltonian path, rooted trees, traversals.

SS-2105T:  Expository Writing (Cr Hr 3+0)

(Prerequisite: none)

Principles of writing good English, understanding the composition process: writing clearly; words, sentence and paragraphs; Comprehension and expression; Use of grammar and punctuation. Process of writing, observing, audience collecting, composing, drafting and revising, persuasive writing, reading skills, listening skills and comprehension, skills for taking notes in class, skills for exams; Business communications; planning messages, writing concise but with impact. Letter formats, mechanics of business, letter writing, letters, memo and applications, summaries, proposals, writing resumes, styles and formats, oral communications, verbal and non-verbal communication, conducting meetings, small group communication, taking minutes. Presentation skills; presentation strategies, defining the objective, scope and audience of the presentation, material gathering material organization strategies, time management, opening and concluding, use of audio-visual aids, delivery and presentation.

CS-2106T: Computer Organization & Assembly Language (Cr Hr 2+1)

(Prerequisite: EE-1201T)

Introduction to computer systems: Information is bits + context, programs are translated by other programs into different forms, it pays to understand how compilation systems work, processors read and interpret instructions stored in memory, caches matter, storage devices form a hierarchy, the operating system manages the hardware, systems communicate with other systems using networks; Representing and manipulating information: information storage, integer representations, integer arithmetic, floating point; Machine-level representation of programs: a historical perspective, program encodings, data formats, accessing information, arithmetic and logical operations, control, procedures, array allocation and access, heterogeneous data structures, putting it together: understanding pointers, life in the real world: using the gdb debugger, outof-bounds memory references and buffer overflow, x86-64: extending ia32 to 64 bits, machine-level representations of floating-point programs; Processor architecture: the Y86 instruction set architecture, logic design and the Hardware Control Language (HCL), sequential Y86 implementations, general principles of pipelining, pipelined Y86 implementations.

CS-2207T: Theory of Automata & Formal Languages (Cr Hr 3+0)

(Prerequisite: none)

Finite State Models: Language definitions preliminaries, Regular expressions/Regular languages, Finite automata (FAs), Transition graphs (TGs), NFAs, Kleene’s theorem, Transducers (automata with output), Pumping lemma and non-regular language Grammars and PDA: CFGs, Derivations, derivation trees and ambiguity, Simplifying CFLs, Normal form grammars and parsing, Decidability, Context sensitive languages, grammars and linear bounded automata (LBA), Chomsky’s hierarchy of grammars Turing Machines Theory: Turing machines, Post machine, Variations on TM, TM encoding, Universal Turing Machine, Defining Computers by TMs.

CS-2208T: Introduction to Operating Systems (Cr Hr 2+1)

(Prerequisite: CS-2104T)

Operating systems basics, system calls, process concept and scheduling, inter-process communication, multithreaded programming, multithreading models, threading issues, process scheduling algorithms, thread scheduling, multiple-processor scheduling, synchronization, critical section, synchronization hardware, synchronization problems, deadlocks, detecting and recovering from deadlocks, memory management, swapping, contiguous memory allocation, segmentation & paging, virtual memory management, demand paging, thrashing, memory-mapped files, file systems, file concept, directory and disk structure, directory implementation, free space management, disk structure and scheduling, swap space management, system protection, virtual machines, operating system security.

CS-2209T: Database Systems (Cr Hr 3+1)

(Prerequisite: none)

Basic database concepts, Database approach vs file based system, database architecture, three level schema architecture, data independence, relational data model, attributes, schemas, tuples, domains, relation instances, keys of relations, integrity constraints, relational algebra, selection, projection, Cartesian product, types of joins, normalization, functional dependencies, normal forms, entity relationship model, entity sets, attributes, relationship, entity-relationship diagrams, Structured Query Language (SQL), Joins and sub-queries in SQL, Grouping and aggregation in SQL, concurrency control, database backup and recovery, indexes, NoSQL systems.

CS-2210T: Software Engineering (Cr Hr 3+0)

(Prerequisite: none)

Nature of Software, Overview of Software Engineering, Professional software development, Software engineering practice, Software process structure, Software process models, Agile software Development, Agile process models, Agile development techniques, Requirements engineering process, Functional and non-functional requirements, Context models, Interaction models, Structural models, behavioral models, model driven engineering, Architectural design, Design and implementation, UML diagrams, Design patterns, Software testing and quality assurance, Software evolution, Project management and project planning, configuration management, Software Process improvement.

MT-2204: Multivariable Calculus (Cr Hr 3+0)

(Prerequisite: MT-1202T)

Functions of Several Variables and Partial Differentiation. Multiple Integrals, Line and Surface Integrals. Green’s and Stoke’s Theorem. Fourier Series: periodic functions, Functions of any period P-2L, Even & odd functions, Half Range expansions, Fourier Transform; Laplace Transform, Z-Transform.

CS-2211T: Compiler Construction (Cr Hr 2+1)

(Prerequisite: CS-2207T)

Compiler Techniques and Methodology: Organization of Compilers, Lexical and Syntax Analysis, Parsing techniques, Object code generation and optimization, detection and recovery from errors. Contrast between compilers and interpreters.

SS-2106T: Technical Report Writing (Cr Hr 3+0)

(Prerequisite: SS-2105T)

Overview of technical reporting, use of library and information gathering, administering questionnaires, reviewing the gathered information; Technical exposition; topical arrangement, exemplification, definition, classification and division, casual analysis, effective exposition, technical narration, description and argumentation, persuasive strategy, Organizing information and generation solution: brainstorming, organizing material, construction of the formal outline, outlining conventions, electronic communication, generation solutions. Polishing style: paragraphs, listening sentence structure, clarity, length and order, pomposity, empty words, pompous vocabulary, document design: document structure, preamble, summaries, abstracts, table of contents, footnotes, glossaries, cross-referencing, plagiarism, citation and bibliography, glossaries, index, appendices, typesetting systems, creating the professional report; elements, mechanical elements and graphical elements. Reports: Proposals, progress reports, Leaflets, brochures, handbooks, magazines articles, research papers, feasibility reports, project reports, technical research reports, manuals and documentation, thesis. Electronic documents, Linear verses hierarchical structure documents.

MT-3105T: Probability and Statistics (Cr Hr 3+0)

(Prerequisite: none)

Introduction to Statistics and Data Analysis, Statistical Inference, Samples, Populations, and the Role of Probability. Sampling Procedures. Discrete and Continuous Data. Statistical Modeling. Types of Statistical Studies. Probability: Sample Space, Events, Counting Sample Points, Probability of an Event, Additive Rules, Conditional Probability, Independence, and the Product Rule, Bayes’ Rule. Random Variables and Probability Distributions. Mathematical Expectation: Mean of a Random Variable, Variance and Covariance of Random Variables, Means and Variances of Linear Combinations of Random Variables, Chebyshev’s Theorem. Discrete Probability Distributions. Continuous Probability Distributions. Fundamental Sampling Distributions and Data Descriptions: Random Sampling, Sampling Distributions, Sampling Distribution of Means and the Central Limit Theorem. Sampling Distribution of S2, t-Distribution, FQuantile and Probability Plots. Single Sample & One- and Two-Sample Estimation Problems. Single Sample & One- and Two-Sample Tests of Hypotheses. The Use of PValues for Decision Making in Testing Hypotheses (Single Sample & One- and Two Sample Tests), Linear Regression and Correlation. Least Squares and the Fitted Model, Multiple Linear Regression and Certain, Nonlinear Regression Models, Linear Regression Model Using Matrices, Properties of the Least Squares Estimators.

CS-2213T: Computer Networks (Cr Hr 2+1)

(Prerequisite: none)

Introduction and protocols architecture, basic concepts of networking, network topologies, layered architecture, physical layer functionality, data link layer functionality, multiple access techniques, circuit switching and packet switching, LAN technologies, wireless networks, MAC addressing, networking devices, network layer protocols, IPv4 and IPv6, IP addressing, sub netting, CIDR, routing protocols, transport layer protocols, ports and sockets, connection establishment, flow and congestion control, application layer protocols, latest trends in computer networks.

CS-3701T: Web Engineering (Cr Hr 2+1)

(Prerequisite: none)

Web programming languages (e.g., HTML5, CSS 3, Java Script, PHP/JSP/ASP.Net), Design principles of Web based applications, Web platform constraints, Software as a Service (SaaS), Web standards, Responsive Web Design, Web Applications, Browser/Server Communication, Storage Tier, Cookies and Sessions, Input Validation, Full stack state management, Web App Security – Browser Isolation, Network Attacks, Session Attacks, Large scale applications, Performance of Web Applications, Data Centers, Web Testing and Web Maintenance.

CS-2214T: Artificial Intelligence (Cr Hr 2+1)

(Prerequisite: none)

An Introduction to Artificial Intelligence and its applications towards Knowledge Based Systems; Introduction to Reasoning and Knowledge Representation, Problem Solving by Searching (Informed searching, Uninformed searching, Heuristics, Local searching, Minmax algorithm, Alpha beta pruning, Game-playing); Case Studies: General Problem Solver, Eliza, Student, Macsyma; Learning from examples; ANN and Natural Language Processing; Recent trends in AI and applications of AI algorithms. Python programming language will be used to explore and illustrate various issues and techniques in Artificial Intelligence.

CS-2215T: Information Security (Cr Hr 2+1)

(Prerequisite: none)

Information security foundations, security design principles; security mechanisms, symmetric and asymmetric cryptography, encryption, hash functions, digital signatures, key management, authentication and access control; software security, vulnerabilities and protections, malware, database security; network security, firewalls, intrusion detection; security policies, policy formation and enforcement, risk assessment, cybercrime, law and ethics in information security, privacy and anonymity of data.

MT-3206T: Numerical Computing (Cr Hr 3+0)

(Prerequisite: none)

Mathematical preliminaries and error analysis, round-off errors and computer arithmetic, Calculate Divided Differences. Use Divided-difference Table. Find Newton’s Interpolation Polynomial. Calculate Interpolation with Equally Spaced Data. Find the Difference Table. Calculate, Newton’s Forward & Backward Difference Formulae. Use Gauss Formulae. Use Stirling’s Interpolation Formula. Use Bessel’s Interpolation Formula. Use Everett’s Interpolation Formula. Solve Nonlinear Equations. Solve Equations by Bisection Method. Solve Equations by Regula Falsi Method. Solve Equations by Secant Method. Solve Equations by Newton-Raphson Method. Find Fixed Point Iteration. Solve Equations by Jacobi Iterative Methods. Solve Equations by Gauss Seidel Method Calculate Numerical Differentiation. Find Numerical Differentiation Formulae Based on Equally Spaced Data. Find Numerical Differentiation Based on Newton’s Forward Differences. Find Numerical Differentiation Based on Newton’s Backward Differences. Find Numerical Differentiation Based on Stirling’s Formula. Find Numerical Differentiation Based on Bessel’s Formula. Find Numerical Differentiation Based on Lagrange’s Formula. Calculate Error Analysis of Differentiation Formulae. Solve Richardson Extrapolation. Calculate Numerical Integration. Use Trapezoidal Rule with Error Term. Use Simpson’s 1/3 Rule with Error Term. Use Simpson’s 3/8 Rule with Error Term. Use Composite Numerical Integration. Use Composite Trapezoidal Rule. Use Composite Simpson’s Rule. Find Richardson’s Extrapolation. Find Newton-Cotes Closed Quadrature Formulae.

CS-2216T: Design & Analysis of Algorithms (Cr Hr 3+0)

(Prerequisite: none)

Introduction; role of algorithms in computing, Analysis on nature of input and size of input Asymptotic notations; Big-O, Big Ω, Big Θ, little-o, little-ω, Sorting Algorithm analysis, loop invariants, Recursion and recurrence relations; Algorithm Design Techniques, Brute Force Approach, Divide-and-conquer approach; Merge, Quick Sort, Greedy approach; Dynamic programming; Elements of Dynamic Programming, Search trees; Heaps; Hashing; Graph algorithms, shortest paths, sparse graphs, String matching; Introduction to complexity classes.

CS-4150P: Final Year Project (Cr Hr 0+3)

To give the students the chance for enhancing their theoretical and practical knowledge in the field of research and development.

MG-1201: Economics and Management (Cr Hr 3+0)

(Prerequisite: none)

Introduction: Basic concept and Principles of Economics, Microeconomic theory, the problems of scarcity, Concept of Engineering Economy.

Economic Environment: Consumer and producer goods, goods and services, demand & supply concept. Equilibrium, elasticity of demand, elasticity of supply, measures of Economic worth. Price-supply-demand relationships. Perfect competition, monopoly, monopolistic competition and oligopoly, Fundamentals of Marketing. Elementary Financial Analysis: Basic accounting equation. Development and interpretation of financial statement-Income statement, Balance sheet and cash flow. Working capital management. Break Even Analysis: Revenue/cost terminologies, behavior of costs. Determination of costs/revenues. Numerical and graphical presentations. Practical applications. BEA as a management tool for achieving financial / operation efficiency.

Selection Between Alternatives: Time value of money and financial internal rate of return. Present Value, future value and annuities. Cost-benefit analysis, selection amongst materials, techniques, design etc.Investment philosophy. Investment alternatives having identical lives. Alternatives having different lives. Make or buy decisions and replacement decisions.

Value Analysis/Value Engineering: Value analysis procedures. Value engineering procedures. Value analysis versus value engineering. Advantages and applications in different areas. Value analysis in designing and purchasing. Linear Programming problems, graphic solution simplex procedure. Duality problem.

Depreciation and Taxes: Depreciation concept, economic life, methods of depreciations, profit and returns on capital, productivity of capital gain (loss) on the disposal of an asset, depreciation as a tax shield. Business Organization: Type of ownership, single ownership, partnerships, corporation, type of stocks and joint stock companies banking and specialized credit institutions. Capital Financing & Allocation: Capital budgeting, allocation of capital among independent projects, financing with debt capital, financing with equity capital trading on equity, financial leveraging.

SS-4109: Entrepreneurship (Cr Hr 3+0)

(Prerequisite: none)

Entrepreneurship and the Entrepreneurial mind-set. Entrepreneurial intentions and corporate Entrepreneurship. Entrepreneurial strategy. Generating exploiting new entries.  Creativity and the business ideas. Identifying and analyzing domestic and international opportunities. Intellectual property and other legal issues for the Entrepreneur. The business plan. Creating and starting the venture. The Marketing plan. The Organizational plan. The Financial plan. Sources of capital. Informal risk capital, venture capital and going public. Strategies for growth and managing the implication of growth. Succession planning and strategies for harvesting and ending the venture.

Course Description of Elective Courses of BS (CS)

Stream-1. Artificial Intelligence

CS-3301T Machine Learning (Cr Hr 2+1)

(Prerequisite: none)

Introduction to machine learning; concept learning: General-to-specific ordering of hypotheses, Version spaces Algorithm, Candidate elimination algorithm; Supervised Learning: decision trees, Naive Bayes, Artificial Neural Networks, Support Vector Machines, Overfitting, noisy data, and pruning, Measuring Classifier Accuracy; Linear and Logistic regression; Unsupervised Learning: Hierarchical Aglomerative Clustering. k-means partitional clustering; Self-Organizing Maps (SOM) k-Nearest-neighbor algorithm; Semi supervised learning with EM using labeled and unlabeled data; Reinforcement Learning: Hidden Markov models, Monte Carlo inference Exploration vs. Exploitation Trade-off, Markov Decision Processes; Ensemble Learning: Using committees of multiple hypotheses. Bagging, boosting.

CS-4305T Deep Learning (Cr Hr 2+1)

(Prerequisite: none)

Basics of deep learning, learning networks, Shallow vs. Deep learning etc.; Machine learning theory – training and test sets, evaluation, etc. Theory of Generalization; Multi-layer perceptrons, error back-propagation; Deep convolutional networks, Computational complexity of feed forward and deep convolutional neural networks; Unsupervised deep learning including auto-encoders; Deep belief networks; Restricted Boltzman Machines; Deep Recurrent Neural Networks (BPTT, LSTM, etc.); GPU programming for deep learning CuDNN; Generative adversarial networks (GANs); Sparse coding and auto-encoders; Data augmentation, elastic distortions, data normalization; Mitigating overfitting with dropout, batch normalization, dropconnect; Novel architectures, ResNet, GoogleNet, etc

CS-4302T Programming for Artificial Intelligence (Cr Hr 2+1)

(Prerequisite: CS-2214T)

Introduction to Programming language (Python): The first objective of the course is to introduce and then build the proficiency of students in the programming language. The basics include IDE for the language (e.g., Jupyter Notebook or IPython), variables, expressions, operands and operators, loops, control structures, debugging, error messages, functions, strings, lists, object-oriented constructs and basic graphics in the language. Special emphasis is given to writing production quality clean code in the programming language using version control (git and subversion). Introducing libraries/toolboxes necessary for data analysis: The course should introduce some libraries necessary for interpreting, analyzing and plotting numerical data (e.g., NumPy, MatPlotLib, Anaconda and Pandas for Python) and give examples of each library using simple use cases and small case studies.

CS-4303T  Natural Language Processing (Cr Hr 2+1)

(Prerequisite: none)

Introduction & History of NLP, Parsing algorithms, Basic Text Processing, Minimum Edit Distance, Language Modeling, Spelling Correction, Text Classification, Deterministic and stochastic grammars, CFGs, Representing meaning /Semantics, Semantic roles, Semantics and Vector models, Sentiment Analysis, Temporal representations, Corpus-based methods, N-grams and HMMs, Smoothing and backoff, POS tagging and morphology, Information retrieval, Vector space model, Precision and recall, Information extraction, Relation Extraction (dependency, constituency grammar), Language translation, Text classification, categorization, Bag of words model, Question and Answering, Text Summarization.

CS-4304T Knowledge Representation & Reasoning (Cr Hr 3+0)

(Prerequisite: none)

Knowledge representation is one of the fundamental areas of Artificial Intelligence. It is the study of how knowledge about the world can be represented and manipulated in an automated way to enable agents to make intelligent decisions. This course will provide an overview of existing knowledge representation frameworks developed within AI including but not limited to propositional and first-order logic, ontologies, planning, reasoning and decision making under uncertainty. The assignments component of the course would provide hands-on experience of software like Prolog, Protégé, probabilistic reasoning APIs and tools to support complex decision making. It is expected that after completing this course, students will understand (a) the foundations of Knowledge Representation & Reasoning and (b) which tools and techniques are appropriate for which tasks.

Stream-2. Data Science & Analytics

CS-4404T Big Data Analytics (Cr Hr 2+1)

(Prerequisite: none)

Introduction and Overview of Big Data Systems; Platforms for Big Data, Hadoop as a Platform, Hadoop Distributed File Systems (HDFS), Map Reduce Framework, Resource Management in the cluster (YARN), Apache Scala Basic, Apache Scala Advances, Resilient Distributed Datasets (RDD), Apache Spark, Apache Spark SQL, Data analytics on Hadoop / Spark, Machine learning on Hadoop / Spark, Spark Streaming, Other Components of Hadoop Ecosystem.

CS-3401T Data Science (Cr Hr 2+1)

(Prerequisite: MT-3105T)

Introduction to Data Science, Big Data and Data Science hype, Datafication, Current landscape of perspectives, Skill sets needed; Statistical Inference: Populations and samples, Statistical modeling, probability distributions, fitting a model, Intro to Python; Exploratory Data Analysis and the Data Science Process; Basic Machine Learning Algorithms: Linear Regression, k-Nearest Neighbors (k-NN), k-means, Naive Bayes; Feature Generation and Feature Selection; Dimensionality Reduction: Singular Value Decomposition, Principal Component Analysis; Mining Social-Network Graphs: Social networks as graphs, Clustering of graphs, Direct discovery of communities in graphs, Partitioning of graphs, Neighborhood properties in graphs; Data Visualization: Basic principles, ideas and tools for data visualization; Data Science and Ethical Issues: Discussions on privacy, security, ethics, Next-generation data scientists.

CS-4405T Platform & Architecture for Data Science (Cr Hr 3+0)

(Prerequisite: none)

An Introduction to Data Architecture; Architecture Shaping the Data through Models. Transformations in the End-State Architecture; Redundant Data; Transformations; Customizing Data; Transforming Text; Transforming Application Data; Transforming Data Into a Customized State; Transforming Data Into Bulk Storage Transforming Data Generated Automatically Transforming Bulk Data; Transformation and Redundancy. A Brief History of Big Data; An Analogy-Taking the High Ground; Taking the High Ground; Standardization With the 360; Online Transaction Processing; Enter Teradata and MPP Processing; Then Came Hadoop and Big Data; IBM and Hadoop; Holding the High Ground.  Parallel Processing. Unstructured Data; Textual Information-Everywhere; Decisions Based on Structured Data; The Business Value Proposition; Repetitive and Non repetitive Unstructured Information; Ease of Analysis; Contextualization; Some Approaches to Contextualization; Map Reduce; Manual Analysis; Contextualizing Repetitive Unstructured Data; Parsing Repetitive Unstructured Data; Recasting the Output Data; Textual Disambiguation; From Narrative Into an Analytical Data Base; Input Into Textual Disambiguation; Mapping; Input / Output.

CS-4406T Data Visualization (Cr Hr 2+1)

(Prerequisite: none)

Introduction of Exploratory Data Analysis and Visualization, Building Blocks and Basic  Operations; Types of Exploratory Graphs, single and multi-dimensional summaries, five  number summary, box plots, histogram, bar plot and others; Distributions, their  representation using histograms, outliers, variance; Probability Mass Functions and their  visualization; Cumulative distribution functions, percentile-based statistics, random  numbers; Modelling distributions, exponential, normal, lognormal, pareto; Probability  density functions, kernel density estimation; Relationship between variables, scatter plots,  correlation, covariance; Estimation and Hypothesis Testing; Clustering using K-means and  Hierarchical; Time series and survival analysis; Implementing concepts with R (or similar.

Stream-3. Network & Cyber Security

CS-3501T Wireless and Security (Cr Hr 2+1)

(Prerequisite: CS-2215T)

Wireless and mobile security overview, design, planning, installation, and maintenance of wireless network security infrastructures. Diagnose distributed denial-of-service attacks and specify mitigation techniques. Vulnerabilities introduced into an infrastructure by wireless and cellular technologies. Security hardening techniques for wireless or mobile technologies. Compare and contrast the needs of law-enforcement versus individual right-to-privacy in wireless infrastructures. Produce a relevant wireless or mobile security team project.

Text Book:

• Jim Doherty; Wireless and Mobile Device Security, Second Edition, Published By; Jones & Bartlett Learning (April 14, 2021)
• Jim Doherty; Wireless and Mobile Device Security, Published By; J ones & Bartlett Learning; 1st edition (January 6, 2019)

CS-4504T Cyber Law & Cyber Crime (Cr Hr 3+0)

(Prerequisite: CS_2215T)

Introduction to cyber-Law & cyber Crime, sociological and socio-legal in content and approach. Different types of internet-related crime; study relevant computing and network technologies, especially where used either in the commission or detection or prevention of cybercrime; analyses policing, legal, electronic, and other measures designed to combat cybercrime and considers their main strengths and weaknesses; and assess recent sociological and socio-legal theories of cyberspace and apply these theories to the specific field of cybercrime. Sex offenders’ use of the internet, computer ‘hacking’; media piracy; the ways in which children might be better protected whilst online and cyber security.

CS-3502T Cloud Computing (Cr Hr 3+0)

(Prerequisite: none)

Introduction to cloud computing, Brief historical overview, Cloud delivery models, Ethics in cloud computing, Cloud and security; Cloud Service providers and the Cloud Ecosystem, Cloud ecosystem, Cloud computing delivery models and service, Examples of modern cloud platforms (e.g. AWS, Google Cloud Platform, Microsoft Azure), Interoperability, Licensing, User experience; Concurrency in the Cloud, Overview of concurrent programming, Communication and concurrency, Coordination and synchronization, Load balancing; Overview of parallel and distributed systems, Overview of parallelism, Parallel architectures, Speedup and scaling, Modular distributed systems; Cloud and Networking, Interconnection for clouds, Scalable communication architectures, Network resource management, Content delivery networks, Ad-hoc networks; Cloud Data Storage, Overview of storage systems and models, Distributed file systems, NoSQL databases, Data storage for online transaction processing systems, Dealing with large data, Reliability; Cloud Applications, Architecting cloud applications, Cloud application development, Workflow pattern, Examples and case studies: commercial applications, research applications, Dealing with software faults; Cloud Resource Virtualization, Virtual machines and hypervisors, File virtualization, Hardware support for virtualization, OS support for virtualization, Performance of virtual machines, VM software and platforms; Cloud Hardware and Software, Virtual machines and containers, Cloud hardware; warehouse-scale computers, Cluster resource management, Container architecture examples; Cloud Resource Management and Scheduling, Policies and mechanisms, Resource utilization and energy efficiency, Application resource management, Models for cloud-based web services, Scheduling algorithms for the cloud; Cloud Security, Cloud security risks, Privacy and trust on the cloud, Cloud data encryption,  Security of cloud infrastructure

CS-4503T Internet of Things (IoT)  (Cr Hr 3+0)

(Prerequisite: none)

Introduction to  Internet of Things (IoT): Machine to Machine / User-less Communication, Common Use Cases, Components of an IoT Solution, Open Source and Commercial Examples, Competing Standards for IoT, IoT specialization: Industrial, Medical/Healthcare, Automotive, Energy/Utilities, Financial; Acquiring Data: Traditional Data Storage, Analog and Digital I/O Basics Sensors and Data Collection Points, Embedded Platforms / Microcontrollers, Software Development, Device Security: Physical and Logical, Connectivity Options, Connecting Sensors to the Cloud, Scaling Number of Sensors; Utilizing Data: Collecting and Storage of IoT Sensor Data, Data Aggregation, Processing IoT Data, Privacy and Security, Analysis and Visualization of Data, How the work together: Cloud and IoT, Big Data and IoT, Use Cases for IoT Data; Implementing IoT: Embedded Operating Systems, Linux and Windows-Based IoT, Cloud-based Data Collection, On-Going IoT Operations; IoT Analytics, ETL (Extract-Transform-Load), Combining IoT Data with Static Data, Scripting and Programming with IoT Data, Machine Learning / Artificial Intelligence; IoT Data Analysis in the Cloud, IoT Strategies, IoT Governance and Management Strategies, Design an IoT Solution.

Stream-4.  Software Engineering

CS-4603T Software Project Management (Cr Hr 3+0)

(Prerequisite: CS-2210T)

            Introduction to Software Project Management (PM), Project Management Concepts, PMI‘s Knowledge areas, PMI Framework, PMI Process Groups. Role of Software Project Manager, Understanding Organizations, Project Evaluation, Initiating a Software Project with Stakeholder Management, Project Planning and Scope Management, Software Project Estimation Techniques, Project Schedule Management for Software Project, Building Project Budget, Software Project Risks, Building Software Project Team, Using Earned Value Management in Software Projects, Procuring Goods and Services, Managing People in Software Environment, Building an Effective Communication Management Plan, Software Quality and Configuration Management, Project Closing.

CS-4604T Software Quality Assurance & Testing (Cr Hr 3+0)

(Prerequisite: CS-2210T)

Software Quality, Software Quality Attributes, Quality Engineering., Testing: Concepts, Issues, and Techniques, Software testing lifecycle., Testing Scopes., Testing Approaches., Testing Concepts., Test Planning Process, Introduction to testing process, Requirement of software test planning, Testing documentation, Reporting and historical data recording., Software testing techniques, Testing philosophies , Testing strategies, Model based testing, Software testing techniques, Testing using models, Domain and combinatorial testing, Unit and integration testing, Acceptance testing, Test automation, Slicing, Software reliability models and engineering, Introduction, Exponential model., Reliability growth models, Modeling process, Software inspections, Software reviews, Inspection checks and metrics, Quality Models, Models for quality assessment, Product quality metrics, Quality Measurements, In-Process metrics for software testing, In-Process quality management, Effort/outcome models, System testing, Introduction to sub-system testing, From functional to system aspects of testing, System testing, Introduction to system testing, Scenarios development, System testing, Use-cases for testing, Specification-based testing, Open issues on software testing.

CS-3903T IS Audit(Cr Hr 3+0)

(Prerequisite: none)

Introduction to Auditing, IS Audit charter, Polices, Procedures, The Audit Process, Audit computer networks and communication, Auditing software development, Acquisition, Maintenance, Auditing IT infrastructure, Auditing Management and Organization, Business process re-engineering: IS audit proposal, report, evidence and follow-up, complaint to standard, Enterprise service agreement, IP pro count policies and process, Backup and procedures, Overview of Computer-Assisted Audit Tools and Techniques.

CS-3602T Human Computer Interaction (Cr Hr 3+0)

(Prerequisite: none)

Contexts for HCI, Psychology of usable things, Processes for User-Centered Design, Metrics and Measures for Evaluation, Usability heuristics and principles of Usability testing, Physical capabilities, Cognitive and social models for interaction design, Principles of good interaction design, Accessibility, Principles of GUI, Visual design elements, Data gathering, Task analysis, Prototyping, Help and user documentation, Internationalization, Usability inspection methods, Usability testing methods, New Interaction Technologies, Usability in practice, Visual Design and Typography, Icon Design, Ubiquitous, Augmented and Virtual Reality.

CS-4605T Design Pattern (Cr Hr 2+1)

(Prerequisite: none)

Software design. Design Patterns. History of design patterns. Refactoring. Testability, domain-specific languages, dependency injection, SOLID. Analyze the relationship between design principles, design patterns, and programming language design, application frameworks, or application domains.

Stream-5. Web Engineering & Social Computing

CS-4706T Web Mining (Cr Hr 3+0)

(Prerequisite: CS-3701T)

Tasks and techniques of Web search and Web mining, i.e., structure mining, content mining, and usage mining. Includes major algorithms from data mining, machine learning, information retrieval and text processing, which are crucial for many Web mining tasks. Supervised learning (specifically in context of web mining), opinion mining and sentiment analysis, recommender systems and collaborative filtering, and query log mining. Applications of web mining techniques to Web and e-commerce data, and their use in Web analytics, user profiling and personalization.

CS-3702T Semantic Web (Cr Hr 2+1)

(Prerequisite: none)

The Semantic Web Activity of W3C: Overview of techniques and standards, XML with Document Type Definitions and Schemas, RDF—The Basis of the Semantic Web, Metadata with RDF (Resource Description Framework), Metadata taxonomies with RDF Schema, Transformation / Inference rules in XSLT, RuleML and RIF, The W3C ontology language OWL, Integrating these techniques for ontology/rule-based multi-agent systems, Semantic Modeling, Semantic Web Applications, Logic for the Semantic Web

CS-4703T Social Computing (Cr Hr 3+0)

(Prerequisite: none)

Theories, technologies and human issues of Web 2.0: how people network online, what networks and communities they form, why they participate and contribute, and how to design infrastructures for successful social applications. Moreover, to survey theoretical and practical instances of social computing such as blogs, social bookmarking, classification and recommendation systems, compare them with traditional professional equivalents, and evaluate how these diverse perspectives can inform one another.

CS-4704T  Web Security and Privacy(Cr Hr 3+0)

(Prerequisite: none)

Risks for Operating Systems, Browsers, and a wide range of current programs and products. Web technology–The technological underpinnings of the modern Internet and the cryptographic foundations of e-commerce, along with SSL (the Secure Sockets Layer), the significance of the PKI (Public Key Infrastructure), and digital identification, including passwords, digital signatures, and biometrics. Web privacy and security for users–Learn the real risks to user privacy, including cookies, log files, identity theft, spam, web logs, and web bugs, and the most common risk, users own willingness to provide e-commerce sites with personal information. Hostile mobile code in plug-ins, ActiveX controls, Java applets, and JavaScript, Flash, and Shockwave programs. Web server security–Administrators and service providers discover how to secure their systems and web services. Topics include CGI, PHP, SSL certificates, law enforcement issues, and more. Web content security–Zero in on web publishing issues for content providers, including intellectual property, copyright and trademark issues, P3P and privacy policies, digital payments, client-side digital signatures, code signing, pornography filtering and PICS, and other controls on web content.

CS-4705T Social Network Analysis (Cr Hr 2+1)

(Prerequisite: none)

Introduction to Network Science, Descriptive Network Analysis, Mathematical Models of Networks, Node Centrality and Ranking on Networks, Network Communities, Python libraries to analyze network data and complex graphs structure, Social network analysis at the network level (e.g. density, clustering, degree distribution, etc.); at the node level (e.g. degree, betweenness, closeness); at the subgraph level (e.g. triads, communities). collect and preprocess network data.  Network Structure and Visualization, Social Media and Information Flow in Networks, Diffusion of Innovation, Institutions and Aggregate Behavior in Networks.