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their
their
security,
Networks.
techniques
techniques
Knowledge
Knowledge
Associating
Beliefs.
Beliefs.
OS
Representation
Representation
Use
Use
KHUZDAR
Expert
(depth
(depth
vs.
vs.
(same as TE outline)
of
of
UET
BALOCHISTAN
converting the following:
first,
first,
probabilities
Systems:
hardening,
representation
representation
Artificial Inteligence 3(3+0)
to
Certainty
Certainty
Declarative
Declarative
breadth
breadth
Eighth Semester
issues,
issues,
Computer Network Security 3(3+0)
Factors
Factors
first,
first,
in
assertions
in
Components
Honeypots
Knowledge,
Knowledge,
in
etc.).
etc.).
of
and
Procedural
Procedural
expert
Rule-Based
Rule-Based
Heuristic
Heuristic
first-order
Reasoning.
Reasoning.
systems,
Facts,
honeynets.
Systems.
Search:
Search:
logic.
Knowledge
Knowledge
Facts,
Systems.
definitions, techniques: hill climbing etc. Knowledge Representation:
Space Search: graph theory, strategies (data driven, goal driven),
and Search: Definition and examples of Production Systems. State
Introduction to Artificial Intelligence, Basic elements of AI, history,
applications and classification of techniques used. Production Systems
Representing Knowledge using Rules, Logic Programming. Common
development methodology (selection of problems, knowledge
engineering), types (rule based, model based, case based), knowledge
representation (rules, semantic networks, frames), inference, forward
Sense and Statistical Reasoning: Nonmonotonic reasoning and modal
logic for nonmonotonic reasoning. How to deal with Agents and
Associating probabilities to assertions in first-order logic. Bayesian
chaining, backward chaining, production systems and rule based
needing to be secured and threats they face. Diffrent types of attacks.
Malicious non-malicious program flaws. Protection in Operiting
Syestems. Spoofing, intrusiond Detection Systems, Firewalls, Device
expert systems. goal driven problem reasoning, data driven reasoning.
The course over various topics in computer network security
The need for network security. The network security problem, assets
Representing Knowledge using Rules, Logic Programming. Common
Introduction to Artificial Intelligence, Basic elements of AI, history,
applications and classification of techniques used. Production Systems
logic for nonmonotonic reasoning. How to deal with Agents and
Sense and Statistical Reasoning: Nonmonotonic reasoning and modal
definitions, techniques: hill climbing etc. Knowledge Representation:
Space Search: graph theory, strategies (data driven, goal driven),
and Search: Definition and examples of Production Systems. State
Final Projct-II 3(3+0)
Digital Signal Processing
Entrepreneurship and Leadership 3 (3+0)
systems especially on DSP chips including fixed point programming.
01
leadership and decision-making; Nature of enterpreneurial work - risks, rewards, challenges.
speed (block) convolution, Z-transform and its properties with inverse, FIR and IIR filters and their
spectrum analysis, power spectrum for random signals, porting of DSP algorithms on embedded
Leadership styles; group and organizational leadership; values and ethics; is leadership born or
Frequency Response, Fourier transform and frequency response, discrete time Fourier transform,
Applications of DSP, digital signals, systems and convolution. Flip and Slide Convolution &
implementations, FIR filter design methods, IIR filter design methods, resolution & side lobes,
leadership principles; Human behavior and motivation in performance; Values and ethics in
learned; management vs. leadership; Enterpreneurship; Individual, group and organizational
symmetry properties, sampling theorem & D/A reconstruction, DFT and FFT algorithms, DFT
properties & Circular Convolution (spectrum analysis & windowing), FFT algorithms and high