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Bachelor of Science in Health Informatics (BS HI)
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| MT-1105T | Business Mathematics | 3+0 | None |
| SS-1204T | Functional English | 3+0 | None |
| CS-1101T | Programming Fundamentals | 3+0 | None |
| CS-1101L | Programming Fundamentals Lab | 0+1 | None |
| SS-1102T / SS-1103T | Islamic Studies / Ethical Behaviour | 2+0 | None |
| HI-1101T | Introduction to Biology | 3+0 | None |
| CS-1102T | Application of Information & Communication Technologies | 2+0 | None |
| CS-1102L | Application of Information & Communication Technologies Lab | 0+1 | None |
| Total | 18 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| SS-2213T | Business Communication | 3+0 | None |
| MT-2106T | Descriptive Statistics | 3+0 | None |
| AC-2101T | Financial Accounting | 3+0 | None |
| CS-1203T | Object Oriented Programming | 3+0 | CS-1101T |
| CS-1203L | Object Oriented Programming Lab | 0+1 | None |
| SS-1101T | Ideology and Constitution of Pakistan | 2+0 | None |
| HI-2202T | Human Physiology and Histology | 2+0 | None |
| HI-2202L | Human Physiology and Histology Lab | 0+1 | None |
| Total | 18 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| SS-2107T | Civics and Community Engagement | 1+0 | None |
| SS-2107L | Civics and Community Engagement Lab | 0+1 | None |
| MK-2104T | Digital Health, Promotion and Marketing Strategies | 3+0 | None |
| CS-2104T | Data Structures & Algorithms | 3+0 | CS-1101T |
| CS-2104L | Data Structures & Algorithms Lab | 0+1 | None |
| HI-2103T | Healthcare Risk Management | 3+0 | None |
| HI-2104T | Patient Safety and Humanities in Hospital | 3+0 | None |
| HI-2105T | Medical Terminologies | 3+0 | None |
| Total | 18 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| EC-2203 | Business Economics | 3+0 | None |
| CS-2209T | Database Systems | 3+0 | None |
| CS-2209L | Database Systems Lab | 0+1 | None |
| AC-3202T | Cost and Management Accounting | 3+0 | AC-2101T |
| SS-4108T | Social Entrepreneurship | 2+0 | None |
| HI-2206T | Strategies of Healthcare Records, Security, Legal and Ethical Issues | 3+0 | None |
| Total | 15 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| CS-1203T | Health Data Classification and Coding Systems | 2+0 | CS-1203T |
| CS-1203L | Health Data Classification and Coding Systems Lab | 0+1 | None |
| SS-3109T | Organizational Behaviour in Hospital Administration | 3+0 | None |
| HI-3109T | Healthcare Project Management | 3+0 | None |
| CS-2215T | Artificial Intelligence in Health Informatics | 2+0 | None |
| CS-2215L | Artificial Intelligence in Health Informatics Lab | 0+1 | None |
| BS-3201T | Research Methods in Healthcare | 3+0 | None |
| Total | 15 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| HI-3211T | Health Data Analytics | 2+0 | None |
| HI-3211L | Health Data Analytics Lab | 0+1 | None |
| HI-3212T | Image Informatics | 3+0 | None |
| HI-3210T | Public Health Informatics | 3+0 | None |
| SS-4115T | Technical and Business Writing | 3+0 | None |
| HI-3213T | Health Care Policy, Issues and Laws | 3+0 | None |
| HI-3214T | Medical Therapeutics and Pharmacology | 3+0 | None |
| Total | 18 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| HI-4150P | Final Year Project – I | 3+0 | None |
| MG-3203T | Health Delivery & Operations Management | 3+0 | None |
| SCM-3205T | Hospital Procurement and Supply Chain Management | 3+0 | None |
| SS-4118T | Ideology and Constitution of Pakistan | 3+0 | None |
| HI-5XXX | HI Elective-I | 3+0 | None |
| HI-5XXX | HI Elective-II | 3+0 | None |
| Total | 15 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| HI-5250P | Final Year Project – II | 3+0 | HI-5150P |
| HI-4216T | Hospital Epidemology | 3+0 | None |
| SCM-6212T | Quality Management in Health | 3+0 | None |
| HI-5XXX | HI Elective-III | 3+0 | None |
| HI-5XXX | HI Elective-IV | 3+0 | None |
| Total | 15 | ||
| Code | Course | Cr Hr | Prerequisites |
|---|---|---|---|
| HI-3801T | Healthcare Decision Making and Support Systems | 3+0 | None |
| HI-3802T | Drug Management | 3+0 | None |
| HI-4803T | Contemporary Issues in Healthcare Management | 3+0 | None |
| HI-4804T | Hospital Infection Control & Biomedical Waste Management | 3+0 | None |
| HI-4805T | Clinical Informatics | 3+0 | None |
| HI-4806T | Nursing Informatics | 3+0 | None |
Course Name: Communication and Presentation Skills / Expository Writing
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: English Composition & Comprehension / Functional English
Course Outline:
Principles of writing good English, understanding the composition process: writing clearly; words, sentences, 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 concisely but with impact. Letter formats, business mechanics, letter writing, letters, memo and applications, summaries, proposals, 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.
Course Name: English Composition & Comprehension / Functional English
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: None
Course Outline:
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.
Course Name: Application of Information and Communication Technologies
Credit Hours: 2-1
Contact Hours: 2-3
Pre-requisites: None
Course Introduction:
This is an introductory course in Computer Science designed for beginners. Apart from leading the participants through a whirlwind history of computing, the course also develops a feel for web programming through a series of lectures that help the students develop their own web page. Main objective of the course is to build an appreciation for the fundamental concepts in computing and to become familiar with popular PC productivity software.
Course Outline:
Brief history of the 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 Computers (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, and 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, Media (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.
Course Name: Islamic Studies
Credit Hours: 2-0
Contact Hours: 2-0
Pre-requisites: None
Course Outline:
Basic Themes of Quran, Introduction to Sciences of Hadith, Introduction to Islamic Law Jurisprudence, Primary & Secondary Sources of Islamic Law, Makken & Madnian life of the Prophet, Islamic Economic System, Political theories, Social System of Islam.
Course Name: Pakistan Studies
Credit Hours: 2-0
Contact Hours: 2-0
Pre-requisites: None
Course Outline:
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.
Course Name: Professional Practices
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: None
Course Outline:
Computing Profession, Computing Ethics, Philosophy of Ethics. The Structure of Organizations, Finance and Accounting, Anatomy of a Software House, Computer Contracts, Intellectual Property Rights, The Framework of Employee Relations Law and Changing Management Practices, Human Resource Management and IT, Health and Safety at Work, Software Liability, Liability and Practice, Computer Misuse and the Criminal Law, Regulation and Control of Personal Information. Overview of the British Computer Society Code of Conduct, IEEE Code of Ethics, ACM Code of Ethics, and Professional Conduct, ACM/IEEE Software Engineering Code of Ethics and Professional Practice. Accountability and Auditing, Social Application of Ethics.
Course Name: Technical and Business Writing / Technical Report Writing
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: Communication and Presentation Skills / Expository Writing
Course Outline:
Overview of technical reporting, use of the library and information gathering, and administering questionnaires, reviewing the gathered information; Technical exposition; topical arrangement, exemplification, definition, classification and division, causal analysis, effective exposition, technical narration, description, and argumentation, persuasive strategy, Organizing information and generating solutions: brainstorming, organizing material, construction of the formal outline, outlining conventions, electronic communication, Generation Solutions. Polishing style: paragraphs, listening to 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, magazine articles, research papers, feasibility reports, project reports, technical research reports, manuals and documentation, and thesis. Electronic documents: linear versus hierarchical structure.
Course Name: Calculus and Analytical Geometry
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: None
Course Outline:
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, and 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.
Reference Materials:
Course Name: Differential Equations
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: Calculus and Analytical Geometry
Course Outline:
Ordinary Differential Equations of the First Order: Geometrical Considerations, Isoclines, Separable Equations, Equations Reducible to Separable Form, Exact Differential Equations, Integrating Factors, Linear First-Order Differential Equations, Variation of Parameters. Ordinary Linear Differential Equations; Homogeneous Linear Equations of the Second Order, Homogeneous Second-Order Equations with Constant Coefficients, General Solution, Real Roots, Complex Roots, Double Root of the Characteristic Equation, Differential Operators, Cauchy Equation, Homogeneous Linear Equations of Arbitrary Order, Homogeneous Linear Equations of Arbitrary Order with Constant Coefficients, Nonhomogeneous Linear Equations. Modelling of Electrical Circuits. Systems of Differential Equations. Series Solutions of Differential Equations. Partial Differential Equations: Method of Separation of Variables, wave, Heat & Laplace equations, and their solutions by the Fourier series method.
Course Name: Linear Algebra
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: Calculus and Analytical Geometry
Course Outline:
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
Course Name: Probability and Statistics
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: None
Course Outline:
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, F-Quantile, and Probability Plots. Single Sample & One- and Two-Sample Estimation Problems. Single Sample & One- and Two-Sample Tests of Hypotheses. The Use of P-Values 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.
Course Name: Computer Networks
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: None
Course Outline:
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.
Course Name: Database System
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: None
Course Introduction:
A database systems course introduces fundamental concepts related to the design, implementation, and effective use of databases. It equips students with the knowledge and skills to manage data in various applications. Key topics include database modeling, Structured Query Language (SQL), and database design principles.
Course Outline:
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 subqueries in SQL, Grouping and aggregation in SQL, concurrency control, database backup and recovery, indexes, NoSQL systems.
Course Name: Data Structures and Algorithms
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: Programming Fundamentals
Course Introduction:
A Data Structures and Algorithms course introduction outlines the foundational concepts, common data structures, and essential algorithmic techniques. It typically covers topics like abstract data types, time and space complexity analysis, and various algorithms for sorting, searching, and traversing data structures.
Course Outline:
Abstract data types, complexity analysis, Big Oh notation, Stacks (linked lists and arrays 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.
Course Name: Discrete Structure
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: None
Course Outline:
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. Algorithms, Searching and Sorting Algorithms, elements of graph theory, planar graphs, graph coloring, Graph Algorithms, Euler graph, Hamiltonian path, rooted trees, traversals.
Course Name: Information Security
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: None
Course Outline:
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.
Course Name: Object Oriented Programming
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: Programming Fundamentals
Course Outline:
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.
Course Name: Operating Systems
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: Data Structure and Algorithms
Course Outline:
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
Course Name: Programming Fundamentals
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: None
Course Outline:
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, multidimensional 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.
Course Name: Software Engineering
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: None
Course Outline:
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
Course Name: Analysis of Algorithms
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: Data Structures & Algorithms
Course Outline:
Introduction: Role of algorithms in computing, Analysis on the nature of input, and the 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.
Course Name: Artificial Intelligence
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: Object Oriented Programming
Course Introduction:
Artificial Intelligence has emerged as one of the most significant and promising areas of computing. This course focuses on the foundations of AI and its basic techniques like Symbolic manipulations, Pattern Matching, Knowledge Representation, Decision Making, and appreciating the differences between Knowledge, Data, and Code. The AI programming language Lisp has been proposed for the practical work of this course.
Course Outline:
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; Natural Language Processing; Recent trends in AI and applications of AI algorithms. Lisp & Prolog programming languages will be used to explore and illustrate various issues and techniques in Artificial Intelligence.
Course Name: Computer Organization and Assembly Language
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: Digital Logic Design
Course Outline:
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, out of-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
Course Name: Digital Logic Design
Credit Hours: 3-1
Contact Hours: 3-3
Pre-requisites: None
Course Outline:
Number Systems, Logic Gates, Boolean Algebra, Combination logic circuits and designs, Implementation Methods (K-Map, Quine-McCluskey method), Flip Flops and Latches, Asynchronous and synchronous circuits, Counters, Shift Registers, Counters, Triggered devices & their types. Binary Arithmetic and Arithmetic Circuits, Memory Elements, State Machines. Introduction to Programmable Logic Devices CPLD, FPGA) Lab Assignments using tools such as Verilog HDL/VHDL, MultiSim.
Course Name: Parallel and Distributed Computing
Credit Hours: 2-1
Contact Hours: 2-3
Pre-requisites: Object Oriented Programming, Operating Systems
Course Outline:
Asynchronous/synchronous computation/communication, concurrency control, fault tolerance, GPU architecture and programming, heterogeneity, interconnection topologies, load balancing, memory consistency model, memory hierarchies, Message passing interface (MPI), MIMD/SIMD, multithreaded programming, parallel algorithms & architectures, parallel I/O, performance analysis and tuning, power, programming models (data parallel, task parallel, process-centric, shared/distributed memory), scalability and performance studies, scheduling, storage systems, synchronization, and tools (Cuda, Swift, Globus, Condor, Amazon AWS, OpenStack, Cilk, gdb, threads, MPICH, OpenMP, Hadoop, FUSE).
Course Name: Artificial Neural Networks
Credit Hours: 2-1
Contact Hours: 2-3
Pre-requisites: Programming for Artificial Intelligence
Course Outline:
Introduction and history of neural networks, Basic architecture of neural networks, Perceptron and Adaline (Minimum Error Learning) for classification, Gradient descent (Delta) rule, Hebbian, Neo-Hebbian and Differential Hebbian Learning, Drive Reinforcement Theory, Kohonen Self Organizing Maps, Associative memory, Bi-directional associative memory (BAM), Energy surfaces, The Boltzmann machines, Backpropagation Networks, Feedforward Networks; Introduction to Deep learning and its architecture.
Course Name: Computer Vision
Credit Hours: 2-1
Contact Hours: 2-3
Pre-requisites: Artificial Neural Networks
Course Outline:
Introduction to Computer Vision (Problems Faced, History, and Modern Advancements). Image Processing, Image filtering, Image pyramids, and the Fourier transform, Hough transform. Camera models, setting up a camera model from parameters, Camera looking at a plane, Relationship of plane and horizon line, Rotation about camera centre. Concatenation, Decomposition, and Estimation of transformation from point correspondences, Points and planes in 2D/3D, Transformations in 2D/3D, Rotations in 2D/3D. Edge detection, corner detection. Feature descriptors and matching (HoG features, SIFT, SURF). Applications of Computer Vision Traditional Methods: Image Stitching: Making a bigger picture from smaller pictures Single View Geometry: Converting a single image into a 3D model. Applications of CV using Deep Learning: Image Detection (Localization, Historical Techniques, RCNN, FRCNN, YOLO, Retina), Image Segmentation (UNet, SegNet, MaskRCNN), Image Generation (GANN)
Course Name: Knowledge Representation and Reasoning
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: Artificial Intelligence
Course Outline:
Propositional Logic, First-order Logic, Horn Clauses, Description Logic, Reasoning using Description Logic, Forward and Backward Chaining in Inference Engines, Semantic Networks, Ontologies and Ontology Languages, Logical Agents, Planning, Rule-based Knowledge Representation, Reasoning Under Uncertainty, Bayesian Networks Representation, Inference in Bayesian Networks, Fuzzy Logic, Inference using Fuzzy Rules, Markov Models, Common sense Reasoning, Explainable AI.
Course Name: Machine Learning
Credit Hours: 2-1
Contact Hours: 2-3
Pre-requisites: Programming for Artificial Intelligence
Course Outline:
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 Agglomerative Clustering. k-means partitional clustering; Self-Organizing Maps (SOM), k-Nearest-neighbour algorithm; Semi–supervised learning with EM using labelled and unlabelled 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.
Course Name: Natural Language Processing
Credit Hours: 3-0
Contact Hours: 3-0
Pre-requisites: Artificial Neural Networks
Course Outline:
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 back off, 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
Course Name: Programming for Artificial Intelligence
Credit Hours: 2-1
Contact Hours: 2-3
Pre-requisites: Artificial Intelligence
Course Outline:
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 an 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.
ELIGIBILITY & ADMISSION CRITERIA
Intermediate/12 years schooling/A- Level/equivalent passed is required for admission in BS (Health Informatics) Program.
Program Learning Outcomes
⮚ Upon completion of the program students are expected to:
⮚ Have knowledge to access health data, use it to describe current performance and apply it to healthcare organizations to forecast trends and patterns to improve operations.
⮚ Develop leadership and change management skills.
⮚ Explain the conceptual models of healthcare informatics and how they are used in healthcare organizations to transform care.
⮚ Demonstrate the capacity to make sound and ethical decisions related to healthcare informatics.
⮚ Gain insight into the resources needed to optimize the use of information technology in areas of healthcare research and clinical services delivery.
⮚ Be eligible for entry-level administrative / coordinator positions in healthcare delivery systems, consulting firms, governmental organizations and research organizations.
