COURSE TITLE: Human Resource Management (MS-426)
Credit Hours: 3 (Theory)
Prerequisites: None
COURSE OBJECTIVES:
Organizations succeed through efficient and effective use of resources; central to the resources is human resources. It is therefore imperative to know how organization maintain and retain its human resources. The course is designed to give students insight of theoretical prospective, concepts, issues and practices in human resource management.
COURSE DESCRIPTION:
Introduction to HRM, Human Resource Planning, Job design and analysis, recruitment and selection, motivation and reward system, career planning and development, training and development, performance appraisal, compensation management and employee relation employee health and safety.
Recommended Books:
§ Garry Dessler, Human Resource Management
§ Dale S. Beach, Personel, The Management of people at work
§ Holdin, Human Resource Management
§ William B. Werther and Keith Davis Human Resource and Palnning, McGraw Hill.
download (6).jpg- Teacher: sohrab khan
COURSE TITLE: Digital Signal Processing (CS - 423)
Credit Hours: 3 (Theory) + 1 (Lab)
Pre-requisites: Signals & Systems
COURSE OBJECTIVES:
To introduce concepts of digital filter design and spectrum analysis.
ESSENTIAL TOPICS TO BE COVERED:
· Convolution and frequency response
· Sampling
· Fast Fourier Transform
· Z-transforms
· Digital filters, FIR, IIR
· Industrial Applications
COURSE DESCRIPTION:
Applications of DSP, digital signals, systems and convolution. Flip and Slide Convolution & Frequency Response, Fourier transform and frequency response, discrete time Fourier transform, symmetry properties, sampling theorem & D/A reconstruction, DFT and FFT algorithms, DFT properties & Circular Convolution (spectrum analysis & windowing), FFT algorithms and high speed (block) convolution, Z-transform and its properties with inverse, FIR and IIR filters and their implementations, FIR filter design methods, IIR filter design methods, resolution & side lobes, spectrum analysis, power spectrum for random signals, porting of DSP algorithms on embedded systems especially on DSP chips including fixed point programming.
Recommended Text(s):
- Digital Signal Processing by J. P. Proakis and D. G. Manolakis. 4th Edition, Prentice Hall.
- Digital Signal Processing: A Practical Approach by Emanual C.Ifeachor 2nd edition. Prentice Hall.
download (5).jpg- Teacher: Dr. Zuhaibuddin bhutto
COURSE TITLE: Networks Security & Cryptography (CS - 424)
Credit Hours: 3 (Theory)
Pre-requisites: Computer Communication and Networks
COURSE DESCRIPTION:
Introduction, computer security concepts, OSI security architecture, classical encryption techniques, Block Ciphers and Stream cypher, Passive attacks, active attacks, Symmetric Encryption, RC4, Public key cryptography DES, Triple DES, AES, Key distribution and user authentication, cryptographic hash functions MD5, Digital signatures, Key management and distribution, User authentication protocols, TLS, Malicious software, Firewall, HTTPS, S/MIME, Security Attacks, DoS attacks, Spoofing, Spams, Session Hijacking, Wireless security, Internet Security, Antiviruses, Digital Immune System.
Recommended Books:
§ William Stallings, “Cryptography and Network Security”, 5th edition, Pearson Prentice Hall. (Text Book)
References:
1) Behrouz A. Forouzan, “Cryptography and Network Security”, 2nd edition, McGraw Hill.
2) William Stallings, “Network Security Essentials: Applications and Standards”, 4th edition, Pearson Prentice Hall.
3) Nader Badawy, “Cryptography and Network Security”, 4th edition
download (4).jpg- Teacher: aliraza raza
COURSE TITLE: Artificial Intelligence (CS-421)
Credit Hours: 3 (Theory)
Pre-requisites: Data Structures and Algorithms
COURSE OBJECTIVES:
To introduce the foundations of artificial intelligence.
ESSENTIAL TOPICS TO BE COVERED:
· Expert systems
· Natural language processing
· Knowledge engineering
COURSE DESCRIPTION:
Introduction to Artificial Intelligence, Basic elements of AI, history, applications and
classification of techniques used. Production Systems and Search: Definition and examples of Production Systems. State Space Search: graph theory, strategies (data driven, goal driven), techniques (depth first, breadth first, etc.). Heuristic Search: definitions, techniques: hill climbing etc. Knowledge Representation: Knowledge representation issues, Procedural Knowledge Representation vs. Declarative Knowledge, Reasoning. Facts, Representing Knowledge using Rules, Logic Programming. Common Sense and Statistical Reasoning: Nonmonotonic reasoning
and modal logic for nonmonotonic reasoning. How to deal with Agents and their Beliefs. Use of Certainty Factors in Rule-Based Systems. Associating probabilities to assertions in first-order logic. Bayesian Networks. Expert Systems: Components of expert systems, development methodology (selection of problems, knowledge engineering), types (rule based, model based, case based), knowledge representation (rules, semantic networks, frames), inference, forward chaining, backward chaining, production systems and rule based expert systems. goal driven problem reasoning, data driven reasoning. (same as TE outline)
Recommended Text(s):
- Artificial Intelligence: A Modern Approach, 2nd Ed., Stuart J. Russell and Peter Norvig, Prentice Hall, 2002.
- Artificial Intelligence, 2nd Ed., Elaine Rich and Kevin Knight, McGraw-Hill 1990.
- Artificial Intelligence in Engineering Approach, R. J. Schalkoff, McGraw Hill, 1990.
- Introduction to Expert Systems, 3rd Ed, Peter Jackson, Addison Wesley, 1998.
- Prolog Programming for Artificial Intelligence, 3rd Ed., Ivan Bratko, Addison Wesley 2000.
- Under Standing Artificial Intelligence by Henry C. Mishkof.
- Artificial Intelligence by Luger.
51c2fftrE6L.jpg- Teacher: Gul Nissa Kurdi
- Teacher: Dr. Shabbar Naqvi