PU(Maubin), Faculty of Computing's Learning Management System

Course Objectives
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%)

Teacher: htwe htwe aung
Teacher: Ei Sandar Myint

CST-4242 Modeling and Simulation

Teacher: yin yinhtay

CS-4223 Software Design and Development

Teacher: zuzu aung
Teacher: San San Maw

CS-4214 Advanced Artificial Intelligence

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%

Teacher: htwe htwe aung
Teacher: lwin may thant

CS-4225 Advanced Database and Development

Teacher: zuzu aung
Teacher: San San Maw

CS-4216 Strategies for Emerging Technology

Teacher: eiei aung
Teacher: yuwai hlaing
Teacher: Ei Sandar Myint

CS-4257 Digital Business and E-commerce Management

Teacher: winemohmoh htun
Teacher: khinsan wai

CST-4211 Parallel and Distributed Computing

Teacher: San San Maw
Teacher: Admin User

CST-4242 Modeling and Simulation

Teacher: Admin User
Teacher: yin yinhtay

CT-4233 Cryptography and Information Security

Teacher: dr.khin swe swe myint
Teacher: Admin User

CT-4234 Embedded System Integrating IoT

Course Objectives

▪ 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.