James Broad,” Risk Management Framework”, 1st Edition , Syngress , 2013, ISBN-10: 9780124047235 ,
ISBN-13: 9781597499958
Brief list of topics to be covered
• Introduction to information security Inspection
• Resource inventory
• Threat assessment
• Identifying vulnerabilities
• Assigning safeguards Protection
• Awareness
• Access
• Identification
• Authentication
• Authorization
• Availability
• Accuracy
• Confidentiality
• Accountability Administration Detection
• Intruder types
• Intrusion methods
• Intrusion process
• Detection methods
• Monitoring systems Reaction
• Incident determination
• Incident notification
• Incident containment
11/19/2020 81
• Assessing damage
• Incident recovery
• Cyber incident planning: the business case
• Automated response Reflection
• Incident documentation
• Incident evaluation
• Legal prosecution Risk assessment frameworks
• COSO Integrated Control Framework
• CoBiT – ISACA
• Australia/New Zealand Standard – Risk Management
• ISO Risk Management – Draft Standard Security engineering
• Protocols
• Passwords
• Access controls
• Cryptography Physical aspects
• Biometrics
• Physical tamper resistance
• Security printing and seals
Security in connected systems and networks
• Distributed systems
• Telecom system security
• Network attack and defense
• Protecting e-commerce systems Policy and management issues
• Copyright and privacy protection
• E-policy
11/19/2020 82
Course Name Natural Language Processing ةيعيبطلاةغللاةجلاعم
Course Information
Course
Code Course No Credit Unit
Lec Lab Tot Contact Hours*
Lec Lab
ARTI 501 2 1 3 2 2
*Every contact hour equals minimum 50 minutes Track
University Requirement College Requirement Program Requirement Core
Program
Artificial Intelligence (ARTI) Computer Science (CS)
Computer Information Systems (CIS) Cyber Security and Digital Forensics (CYS)
Level 8 Prerequisite ARTI 401
Course Description
The course is presented in a standard format of lectures, readings, problem sets. It covers the introduction of fundamental concepts and methods for natural language processing using a computer.
Course Learning Outcomes (CLOs)
Upon successful completion of the course, students are expected to be able to:
1. Identify the challenges of semantic representation (SO:0; PI:0.2).
2. Apply classic and stochastic algorithms for parsing natural language. (SO:7; PI:7.1).
3. Apply techniques for information retrieval, language translation and text classification (SO:7; PI:7.1).
4. Design NLP application (SO:7; PI:7.2).
5.
C
ommunicate effectively both in oral and written form (SO:3; PI:3.1)Grading (Assessment Strategies)
Quiz(zes) 10% Assignments
Project(s) 15% Lab 15%
Mid-term 20% Final 40%
Participation Textbook
1. Speech and Language Processing 2nd Edition by Daniel Jurafsky (Author), James H. Martin (Author), Publisher: Prentice Hall; 3rd Edition (May 16, 2008) ISBN-10: 9780131873216, ISBN-13: 978-0131873216.
Reference Book
- Liddy, Elizabeth D. "Natural language processing." (2001).
- Kao, Anne, and Steve R. Poteet, eds. Natural language processing and text mining. Springer Science & Business Media, 2007.
- Introduction to Natural Language Processing: Concepts and Fundamentals for Beginners 1st Edition, Michael Walker (Author)
Brief list of topics to be covered N-gram Language Models Vector Semantic
Part of Speech Tagging and Sequence Labeling Syntactic Parsing
Semantic Analysis Information Extraction Machine Translation Text Mining
11/19/2020 83
Course Name Deep Learning قيمعملعت
Course Information
Course Code Course No Credit Unit
Lec Lab Tot Contact Hours*
Lec Lab
ARTI 502 3 3 3
*Every contact hour equals minimum 50 minutes Track
University Requirement College Requirement Program Requirement Core
Program
Artificial Intelligence (ARTI) Computer Science (CS)
Computer Information Systems (CIS) Cyber Security and Digital Forensics (CYS)
Level 10 Prerequisite ARTI 406
Course Description
In many real-world Machine Learning tasks, especially those with perceptual input, such as vision and speech, the mapping from raw data to the output is often an overly complicated function with many factors of variation. In the past, to achieve acceptable performance on such tasks, significant effort had to be expended to engineer hand crafted features. However, with the advent of deep learning, such tasks have been made easily facilitated and more realistic. In this respect, deep Learning algorithms aim to learn feature hierarchies with features at higher levels in the hierarchy formed by the composition of lower- level features. This automatic feature learning has been demonstrated to uncover underlying structure in the data leading to state-of-the-art results in tasks in vision, speech and rapidly in other domains as well.
This course centers on learning the basic theory of deep learning and how to apply it to various applications. It aims to present the mathematical, statistical, and computational challenges of building stable representations for high-dimensional data, such as images, text, and data. It will delve into selected topics of Deep Learning, discussing recent models from both supervised and unsupervised learning. Special emphasis will be on convolutional architectures, invariance learning, unsupervised learning, and non-convex optimization and other related topics in the area of deep learning.
Course Learning Outcomes (CLOs)
Upon successful completion of the course, students are expected to be able to:
1. Define deep learning and its related fundamental concepts (SO:0; PI:0.1).
2. Describe the main techniques in deep learning and how deep learning works (SO:0; PI:0.1).
3. Design and implement deep neural network systems using deep learning toolkits (SO:7; PI:7.1).
4. Apply deep learning to a real-life project or problem (i.e., solve real life problem using deep learning) (SO:7; PI:7.2).
5. Demonstrate ability to communicate effectively through scientific and technical documentation/presentation describing project activities and outcomes (SO:3; PI:3.1).
6. Demonstrate ability to work effectively in a team tasked with identifying relevant problems to be solved using covered concepts in this course (SO:3; PI:3.2).
Grading (Assessment Strategies)
Quiz(zes) 5% Assignments
10%
Project(s) 20% Lab
Mid-term 20% Final 40%
Participation 5%
Textbook
1. Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron CourvilleMIT Press, 2016 ISBN-10: 0262337436
ISBN-13: 9780262337434 Reference Book
- Neural Networks and Deep Learning by Michael NielsenOnline book, 2016
- Learning Deep Architectures for AI (slightly dated) By Yoshua BengioNOW Publishers, 2009 Brief list of topics to be covered
Part 1: General introduction and overview
• Machine Learning Concepts Reviews
• pattern recognition
11/19/2020 84
• Semi-supervised learning,
• Self-organizing maps,
• distributional clustering
• Reinforcement learning
• Advanced topics on hybrid and ensemble machine learning techniques
• Deep learning Vs Reinforcement learning
• More details on Reinforcement learning
• Introduction to Deep Learning
• Feedforward Deep Networks
• Backpropagation
Part 2: Convolutional Neural Networks
• Invariance, stability.
• Variability models (deformation model, stochastic model).
• Scattering networks
• Group Formalism
• Supervised Learning: classification.
• Properties of CNN representations: invertibility, stability, invariance.
• covariance/invariance: capsules and related models.
• Connections with other models: dictionary learning, LISTA.
• Other tasks: localization, regression.
• Embeddings (DrLim), inverse problems
• Extensions to non-euclidean domains
• Dynamical systems: RNNs.
• Visualizing Convolutional Networks
• Variants (Locally Connected Networks, Tiled CNNs, Dilated Convolutions)
• CNNs on Graphs Introduction
• CNNs on Non-Euclidean Domains
• Locally Connected Networks
• Spectral Networks
• Graph Embedding: Basic
• Message Passing Neural Networks Part 3: Deep Unsupervised Learning
• Autoencoders (standard, denoising, contractive, etc etc)
• Variational Autoencoders
• Adversarial Generative Networks
• Maximum Entropy Distributions Part 4: Miscellaneous Topics
• Non-convex optimization for deep networks
• Stochastic Optimization
• Attention and Memory Models
• Open Problems
11/19/2020 85 Course Name Parallel Computer Architecture and Programming ةيزاوتملابوساحلاةجمربوةسدنه
Course Information
Course Code Course
No Credit Unit Lec Lab Tot Contact
Hours*
Lec Lab
ARTI 503 3 3 3
*Every contact hour equals minimum 50 minutes Track
University Requirement College Requirement Program Requirement Core
Program
Artificial Intelligence (ARTI) Computer Science (CS)
Computer Information Systems (CIS) Cyber Security and Digital Forensics (CYS)
Level 9 Prerequisite ARTI 406
Course Description
This course begins by introducing the concepts of parallel computing architecture and programming models. It covers applications that rely on parallel computing including embarrassingly parallel and synchronous computations. The course introduces the design of modern parallel computing systems (such as multi-core CPUs, SMP (Symmetric Multiprocessing), and GPU architectures) along with parallel programming techniques that can be applied effectively on such machines. To have a deep understanding of parallel computing applications, this course gives knowledge on evaluation and analysis for Parallel computing and Multicore Systems; measuring the influence of communication and parallelism on algorithm design and overall performance of parallel applications. Students are also required to implement and evaluate different types of applications on parallel systems using different programming libraries such as Message Passing Interface (MPI), CUDA, and MapReduce/Hadoop.
Course Learning Outcomes (CLOs)
Upon successful completion of the course, students are expected to be able to:
1. Identify the parallel architecture components for hardware and software approaches (SO:0; PI:0.2).
2. Describe the fundamentals of parallel computers and efficient programming models (SO:0; PI:0.2).
3. Evaluate the performance of parallel systems (SO:6; PI:6.2).
4. Write and evaluate parallel applications using programming libraries on distributed memory and GPU hardware (SO:2; PI:2.1).
5. Apply parallel and distributed algorithms on parallel computing architecture (SO:2; PI:2.2).
6. Write report on latest trends and important approaches of parallel computing (SO:4; PI:4.1).
7. Work as a member of the team and take up leadership position when required (SO:5; PI:5.1).
8. Communicate effectively in oral form (SO:3; PI:3.1).
Grading (Assessment Strategies)
Quiz(zes) 10% Assignments
15%
Project(s) 15% Lab
Mid-term 20% Final 40%
Participation Textbook
1. Roman Trobec, Boštjan Slivnik, Patricio Bulić, and Borut Robič, “Introduction to Parallel Computing”, Springer International Publishing, DOI 10.1007/978-3-319-98833-7, ISBN-10: 3319988328, ISBN-13: 978-3319988320, 1st ed.
2018 edition (September 28, 2018) Reference Book
- David Kirk and Wen-mei Hwu, Programming Massively Parallel Processors: A Hands-on Approach. Third Edition. Morgan Kaufmann. 2016
- William Gropp, Ewing Lusk and Anthony Skjellum, Using MPI: Portable, Parallel Programming with the Message-Passing Interface, 3rd Edition, The MIT Press, November 2014, ISBN: 9780262527392.
- Principles of Parallel Programming, by Calvin Lin and Larry Snyder, Addison-Wesley; 1st edition (March 7, 2008). ISBN:
0321487907
11/19/2020 86 Brief list of topics to be covered
Introduction to Parallel and Distributed Computing parallel algorithms and parallel architectures multiprocessor systems and pipelining.
Shared-memory multiprocessors.
distributed-memory multiprocessor.
Evaluation and Analysis for Parallel computing and Multicore Systems.
Introduction to Message Passing Interface (MPI).
General-purpose Computing using Graphics Processing Units (GPGPU).
Case studies: Matrix Computations, MapReduce/Hadoop.
11/19/2020 87 Course Name Knowledge Representation and Reasoning/Expert
System للادتسلااو ةفرعملا ليثمت
Course Information
Course Code Course
No Credit Unit Lec Lab Tot Contact Hours*
Lec Lab
ARTI 504
3
3 3*Every contact hour equals minimum 50 minutes
Track University Requirement College Requirement Program Requirement
Core Elective
Program
Artificial Intelligence (ARTI) Computer Science (CS)
Computer Information Systems (CIS) Cyber Security and Digital Forensics (CYS)
Level Level 8 Prerequisite
Course Description
The course is presented in a standard format of lectures, readings, problem sets. Students will be introduced to:
1. knowledge representation and reasoning techniques for knowledge-based systems.
2. knowledge-based solution for a given problem.
3. translate a natural language sentence into different knowledge representation forms.
4. Students will work in team on various assessments and projects.
Course Learning Outcomes (CLOs)
After successful completion of this course, students will be able to:
1.
Describe the knowledge representation and reasoning techniques for knowledge-based systems. (SO:0;PI:0.1).2.
Translate a natural language sentence into different knowledge representation forms. (SO:0;PI:0.1).3.
Design and implement a knowledge-based solution for a given problem. (SO:7;PI:7.2).4. Demonstrate a skill to work effectively in a team. (SO:5;PI:5.2).
Grading (Assessment Strategies)
Quiz(zes) 10% Assignments 20%
Project(s) Lab
Mid-term 20% Final 45%
Participation 5%
Textbook
Artificial Intelligence: Foundations of Computational Agents, 2nd edition, Cambridge University Press, 2017.
Reference Book
1. An Introduction to Description Logic. Franz Baader, Ian Horrocks, Carsten Lutz, Uli Sattler
2. Knowledge Representation and Reasoning (The Morgan Kaufmann Series in Artificial Intelligence) 1st Edition, ISBN-10:
9781558609327, ISBN-13: 978-1558609327, Year (June 2, 2004)
3. Frank van Harmelen, Vladimir Lifschitz, Bruce Porter, Handbook of Knowledge Representation (Foundations of Artificial Intelligence) 1st Edition, Elsevier Science; (January 22, 2008), ISBN-10: 0444522115, ISBN-13: 978-0444522115
Brief list of topics to be covered
PART 1: KR&R WITH PROPOSITIONAL AND FIRST ORDER LOGIC
Introduction to knowledge-based technologies and knowledge representation, Expert Systems, Propositional Logic as a simple knowledge representation language, Representing Knowledge in First Order Predicate Logic, Limitations of Propositional and First Order Predicate Logic
PART 2: FRAGMENTS OF FIRST ORDER LOGIC
Description Logics as Knowledge Representation Languages, Reasoning in Description Logics, Lightweight description logics, Horn Fragments of First Order Logic. Rule-based Knowledge Representation and Reasoning, Ontologies and Ontology Languages, Other Decidable Fragments of First Order Logic for Knowledge Representation, Description Logics as Knowledge Representation Languages, Reasoning in Description Logics, Uncertainty in AI and reasoning under uncertainty, Lightweight description logics.
PART 3: NON-MONOTONIC LOGICS
Classical vs non-monotonic logic. Ways to achieve non-monotonicity, Stable Model Semantics
11/19/2020 88
Course Name Project Proposal عو رشم ح ربقم
Course Information
Course Code Course No
Credit Unit Lec Lab Tot Contact Hours*
Lec Lab
ARTI 511 2 2 4
*Every contact hour equals minimum 50 minutes Track
University Requirement College Requirement Program Requirement Core
Program
Artificial Intelligence (ARTI) Computer Science (CS)
Computer Information Systems (CIS) Cyber Security and Digital Forensics (CYS)
Level 9 Prerequisite CS-411, Software
Engineering Course Description
In this course, students select project in Artificial Intelligence domain of their interest and prepare the project proposal including:
• Objectives for the project
• Problem statement
• Literature Survey
• System requirements Specification (SRS)
• Software Designing Specification (SDS)
• Proposed various candidate solutions for the problem of study supported by various feasibility studies for different candidate solutions and select the best candidate solution
Course Learning Outcomes (CLOs)
Upon successful completion of the course, students are expected to be able to 1. List courses from the curriculum used to solve the problem (SO:0; PI:0.1)
2. List methods/techniques from other disciplines used to solve the problem (SO:0; PI:0.2) 3. Formulate requirements specification (SO:1; PI:1.2)
4. Estimate required resources for successful completion of the task (SO:1; PI:1.3) 5. Use mathematical tool to evaluate the performance of algorithms (SO:6; PI:6.1)
6. Apply appropriate algorithmic techniques to solve the given problem that demonstrates comprehension of the tradeoffs involved in design choices (SO:6; PI:6.2)
7. Apply computer science theory in the modeling and design of computer-based systems (SO:6; PI:6.3) 8. Design computer-based systems demonstrating intelligence (SO:7; PI:7.2)
9. Analyze a complex or multidisciplinary problem and apply AI principles to identify solution (SO:7; PI:7.3) 10. Compose a design strategy to meet desired needs of the problem (SO:2; PI:2.1)
11. Demonstrate coordination effectively in a team (SO:5; PI:5.1)
12. Demonstrate the abilities to organize themselves and complete assignment to meet deadlines (SO:5; PI:5.2) 13. Demonstrate the ability to acquire new skills and practice them in realizing a solution (SO:4; PI:4.1)
14. Illustrate the understanding of various ways the computing technology impacts individuals, organizations, and society (SO:4; PI:4.2)
15. Write technical reports to document project activities (SO:3; PI:3.1)
16. Demonstrate effective communication with a range of audience (SO:3; PI:3.2)
Grading (Assessment Strategies)
Bi-weekly Progress report 30 % Assignments
Final Report 45 % Final
Mid-term Report 10 %
Presentation 15 %
Textbook
No prescribed textbook. However, IEEE templates and handouts will be given in classes.
Reference Book
- Lynn E. Miner & Jeremy T. Miner, “Proposal Planning and Writing”, Greenwood Publishing Group; Greenwood; 5th Edition (October 28, 2013), ISBN-10: 1440829691, ISBN-13: 978-1440829697
11/19/2020 89 - Harold Kerzner, Project Management: A Systems Approach to Planning, Scheduling, and Controlling 12th Edition, Wiley;
12th Edition (April 3, 2017), ISBN-10: 9781119165354, ISBN-13: 978-1119165354
- Software Engineering, 10th Edition 10th Edition by Ian Sommerville, Pearson; 10th edition (October 24, 2018), ISBN-10:
9332582696, ISBN-13: 978-9332582699 Brief list of topics to be covered
Not applicable for this course
11/19/2020 90
Course Name Data Science and Analytics تلايلحتلاوتانايبلاملع
Course Information
Course Code Course
No Credit
Unit
Lec Lab Tot Contact
Hours*
Lec Lab
ARTI 506 3 3 3
*Every contact hour equals minimum 50 minutes Track
University Requirement College Requirement Program Requirement Core
Program
Artificial Intelligence (ARTI) Computer Science (CS)
Computer Information Systems (CIS) Cyber Security and Digital Forensics (CYS)
Level 10 Prerequisite NA
Course Description
This course will introduce students to this rapidly growing field of data science and analytics and equip them with some of its basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they need to deal with various facets of data science and analytics practices, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. Emphasis will be placed on integration and synthesis of concepts and their application to solving problems. To contextualize learning in this course, real or production datasets from variety of disciplines shall be explored.
Course Learning Outcomes (CLOs)
Upon successful completion of the course, students are expected to be able to:
1. Describe what Data Science and analytics is and the skill sets needed to be a data scientist/analytic (SO:0; PI:0.1).
2. Describe the Data Science Process and how its components interact (SO:0; PI:0.1).
3. Explain in basic terms what statistical inference means and use designated software to carry out basic statistical modelling and analysis (SO:0; PI:0.1).
4. Explain the significance of exploratory data analysis (EDA) and apply basic visualization tools (plots, graphs, summary statistics, etc.) to carry out EDA (SO:0; PI:0.1).
5. Apply machine learning algorithms (Linear Regression, k-Nearest Neighbors (k-NN), k-means, Naive Bayes etc) for predictive modeling to support data analytics (SO:7; PI:7.1)
6. Demonstrate ability to communicate effectively through scientific and technical documentation/presentation describing project activities and outcomes (SO:3; PI:3.1)
Grading (Assessment Strategies)
Quiz(zes) 5% Assignments
10%
Project(s) 20% Lab
Mid-term 20% Final 40%
Participation 5%
Textbook
1. Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk from The Frontline. O’Reilly. 2014. ISBN-13:
978-1449358655, ISBN-10: 1449358659 Reference Book
- Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press. 2014.
(free online)
- Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 0262018020. 2013.
- Sosulski, K. (2018). Data Visualization Made Simple: Insights into Becoming Visual. New York: Routledge.
- Foster Provost and Tom Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data- analytic Thinking. ISBN 1449361323. 2013.
- Trevor Hastie, Robert Tibshirani and Jerome Friedman. Elements of Statistical Learning, Second Edition. ISBN 0387952845. 2009. (free online)
- Avrim Blum, John Hopcroft and Ravindran Kannan. Foundations of Data Science. (Note: this is a book currently being written by the three authors. The authors have made the first draft of their notes for the book available online. The material is intended for a modern theoretical course in computer science.) https://www.cs.cornell.edu/jeh/book.pdf
11/19/2020 91 Brief list of topics to be covered
Introduction to data science and analytics
• Introduction What Is Data Science
• Big Data and Data Science Hype
• Getting Past the Hype, etc.
• The Data Science Process
Spam Filters, Naive Bayes, and Wrangling Scraping the Web: APIs and Other Tools Time Stamps and Financial Modelling
Exploratory Data Analysis and the Data Science Process - Basic tools (plots, graphs and summary statistics) of EDA - Philosophy of EDA
• Data Wrangling: APIs and other tools for scrapping the Web
• Predictive modeling to support data analytics (selected Machine Learning Algorithms)
• Recommendation Systems: Building a User-Facing Data Product
• Introduction to data visualization
• Data for data graphics
• Design principles (Categorical, time series, and statistical data graphics
• Storytelling
• Multivariate displays
• Geospatial displays
• Dashboards, interactive and animated displays
• Social Networks and Data Journalism
• Mining Social-Network Graphs & Introduction to Text mining & its concepts Data Science and Ethical Issues
• Discussions on privacy, security, ethics
• A look back at Data Science
• Next-generation data scientists
11/19/2020 92
Course Name Project Implementation عو رشملا ذيفنت
Course Information
Course
Code Course No
Credit Unit Lec Lab Tot Contact Hours*
Lec Lab
ARTI 521 3 3 6 0
*Every contact hour equals minimum 50 minutes
Track University Requirement College
Requirement
Program Requirement Core
Program
Artificial Intelligence (ARTI) Computer Science (CS)
Computer Information Systems (CIS) Cyber Security and Digital Forensics (CYS)
Level 10 Prerequisite ARTI 511
Course Description
Project implementation course offers students an opportunity to assemble their knowledge acquired throughout their BS curriculum to realize a final project. This would require them to gather information about the proposed subject and realize a final report as well as to develop a system practically. At this stage, students must carry out all phases of system development for the subject already defined in the precedent course (Project Proposal), and under the supervision of the same supervisor (as possible). At the end of the semester, grading will be obtained based on Final Report, Project Demo and an Oral Presentation of the project to be held by a committee from faculty members.
Course Learning Outcomes (CLOs)
Upon successful completion of the course, students are expected to be able to 1. List courses from the curriculum used to solve the problem (SO:0; PI:0.1)
2. List methods/techniques from other disciplines used to solve the problem (SO:0; PI:0.2) 3. Create appropriate components to effectively manage a project (SO:1; PI:1.1)
4. Write requirements specifications addressing the needs of a problem (SO:1; PI:1.2) 5. Estimate required resources for successful completion of the task (SO:1; PI:1.3) 6. Assess mathematical tools to evaluate the performance of algorithms (SO:6; PI:6.1a)
7. Develop different computer algorithm in building alternative computer-based solutions (SO:6; PI:6.1b) 8. Formulate appropriate algorithmic techniques to solve the given problem (SO:6; PI:6.2a)
9. Devise comprehension of the tradeoffs involved in design choices (SO:6; PI:6.2b)
10. Assess the ability to apply computer science theory in the modeling and design of computer-based systems (SO:6; PI:6.3) 11. Develop intelligent computer-based solution (SO:7; PI:7.1)
12. Design computer-based systems demonstrating intelligence (SO:7; PI:7.2)
13. Analyse a complex or multidisciplinary problem and apply AI principles to identify solution (SO:7; PI:7.3) 14. Compose a design strategy to meet desired needs of the problem (SO:2; PI:2.1)
15. Construct a computer-based solution addressing design specifications (SO:2; PI:2.2) 16. Design testcases to measure the effectiveness of the solution (SO:2; PI:2.3)
17. Demonstrate the abilities to participate in team activities (SO:5; PI:5.1)
18. Formulate appropriate team structure to timely achieve common goals (SO:5; PI:5.2)
19. Demonstrate the ability to acquire new skills and practice them in realizing a solution (SO:4; PI:4.1) 20. Categorize professional, ethical, legal, and social implications related to a proposed system (SO:4; PI:4.2) 21. Write technical reports to document project activities (SO:3; PI:3.1)
22. Demonstrate oral presentation skills using critical and reflective thinking (SO:3; PI:3.2)
Grading (Assessment Strategies)
Bi-weekly Progress
report 30 % Assignments
Project Report 45 % Final
Mid-term Report 10 % Presentation & Demo 15 % Textbook
No prescribed textbook. However, IEEE templates and handouts will be given in classes.
Reference Book