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Course Assessment
- Exam - 60%
- Tutorial and Quiz - 15%
- Project - 25% (to practice the SE disciplines)
- Software Requirement Specification (SRS) Report-8%
- Software Design Documentation (UML) Report-8%
- Developing Small Software Project-9%
- The pass grade for this course is C (50)
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- Teacher: khinsan wai
Assessment Plan
- Exam – 60%
- Quiz – 10%
- Tutorial – 10%
- Practical Lab – 20% (SQL, Triggers, store procedures)
Reference Text Books Images
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Course Organization
The expected learning outcomes for the course will be assessed through six forms of activity:
1. Attending the lectures
2. Preparing for and participating in the recitations.
3. Assignments
4. Reading the text
5. Quiz
6. Exams
Assessment
Assignment 10%
Tutorial 10%
Quizzes / Moodle 15%
Class Participation 5%
Paper based Exam 60%
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Code: CS-4125
Course: Software Project Management
Text Books:
(1) Roger. S. Pressman, “Software Engineering, A Practitioner’s Approach”, 8th edition, 2015
(2) Iron Sommerville, “Software Engineering”, Global Edition, 10th edition, 2016
(3) Rajib Mall, “Fundamentals of Software Engineering”, Fourth Edition, 2014
Course Assessment
This course should assess the student as follows:
Exam – 70%
Tutorial – 20%
Quiz – 10%
The pass grade for this course is – C (50)
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The aim of this course is to help students the fundamental issues of designing and engineering distributed systems and parallel computing architectures. The Distributed systems concerns with reference to partial failure, heterogeneity, scalability, concurrency and asynchrony. This course deals with the major recent developments in distributed systems technology and parallel computing systems.
Learning Outcomes
gain a clear understanding of the concepts that underlie distributed computing systems, along with design and implementation issues
understand key mechanisms and models for distributed systems
identify the core concepts of distributed systems: the way in which several machines orchestrate to correctly solve problems in an efficient, reliable and scalable way
create an awareness of the fundamental technical challenges in advanced distributed systems design and implementation
present the principles underlying the function of distributed systems and their extension to grid and cloud computing and virtualization techniques
Reference Book
Distributed Systems Concepts and Design, 5th Edition, by George Coulouris, Jean Dollimore, Tim Kindberg and Gordon Blair
http://www.cdk5.net/wp/
The Design and Analysis of Parallel Algorithms by Selimm G. Akl
https://computing.llnl.gov/tutorials/parallel_comp/
Course Organization
1. Exam
2. Assignments
3. Tutorial
4. Moodle
5. Project
Assessment Plan for the Course
(Distributed Systems: 80% + Parallel Computing Architectures: 20%)
Paper Exam: (DS-50% + Parallel 15%)
Tutorial/Assignment: 10% (DS 10%)
Project (RMI & COBRA): 10% (DS 10%)
Quizzes: 15% (DS 10% + Parallel 5%)
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Course Objectives
- To provide the most fundamental concept of knowledge representation and reasoning in Artificial Intelligence.
- To provide the ability to analyze and inference upon Uncertain Knowledge conditions
- To understand the basic theory underlying machine learning.
- To gain practical experience through coding exercises, projects, and assignments involving datasets.
Course Description
This course is intended to introduce the basic principles, techniques, and applications of the field of artificial intelligence: Knowledge representation and Uncertain Knowledge and reasoning algorithms. This course provides an introduction to the fundamental concepts and techniques of machine learning. Machine learning is a critical field within artificial intelligence that enables computers to learn from data and make intelligent decisions. Through a combination of theoretical lectures and hands-on practical sessions, students will gain a strong understanding of the principles and methodologies that underlie various machine learning algorithms.
Reference Book
- Artificial Intelligence- A Modern Approach (Third Edition) by Stuart Russell & Peter Norvig
- Ronald J. Brachman and Hector J. Levesque, Knowledge Representation and Reasoning, Elsevier/Morgan Kaufmann 2004. ISBN 1-55860-932-6.
- Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido
Learning Outcomes
- Have knowledge and understanding of the basic concepts of knowledge representation and reasoning.
- Develop a solid understanding of the fundamental concepts, terminology, and principles of machine learning.
- Grasp the difference between supervised, unsupervised, and reinforcement learning.
Course Organization
- Attending the lectures & practical
- Preparing for and participating in the recitations
- Practical assignments
- Moodle (LMS) / Quiz
- Exams
Assessment plan for the course
- Assignment 10%
- Quiz/Moodle 10%
- Tutorial 10%
- Lab 10%
- Exam 60%
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▪ To conceptualize the basics of organizational and architectural issues of a digital
computer.
▪ To study the different ways of communicating with I/O devices and standard I/O
interfaces.
▪ To study the hierarchical memory system including cache memories and virtual
memory.
▪ To study various classes of instruction: data movement, arithmetic, logical, and
flow control.
▪ To appreciate how conditional operations are implemented at the machine level.
▪ To understand the way in which subroutines are called and returns made.
▪ To understand parallelism both in terms of a single processor and multiple
processors.
Learning Outcomes
The major outcomes of this course can be listed as
▪ Ability to perform computer arithmetic operations and control unit operations.
▪ Interpret the difference between hardwired and micro-programmed design approaches in CPU control unit design.
▪ Ability to understand the concept of I/O organization.
▪ Ability to conceptualize instruction level parallelism.
▪ Demonstrate the organization of memory hierarchy.
▪ Understand parallelism both in terms of a single processor and multiple processors.
▪ Understand how computer hardware has evolved to meet the needs of multi-processing systems.
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