Teaching
Within the academic framework at Majmaah University, I take pride in my role as an instructor for bachelor-level track courses in Artificial Intelligence and Data Science, and master's-level courses in Cybersecurity and Data Science.
CS460: Computer Vision
Semester: Spring and Autumn
Offered: 2023/2022/2021
This course provides an introduction to computer vision, including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification, scene understanding, and deep learning with neural networks. Implementation of various algorithms will be done in Python language.
CS462: Machine Learning
Semester: Spring and Autumn
Offered: 2023/2022/2021
The course objective is to study the theory and practice of constructing algorithms that learn from data. Machine learning is a field with goals that overlap with other disciplines such as statistics, algorithms, engineering, or optimization theory. It also has wide applications in several scientific areas such as finance, life sciences, social sciences, and medicine. Python or R Language will be used for the implementation of machine learning algorithms.
CS473: Data Visualization
Semester: Spring and Autumn
Offered: 2023/2022/2021
This course delves into the intricacies of data visualization techniques, employing various plots to illuminate the relationships within datasets. Encompassing a comprehensive exploration of different plots and their significance, the practical implementation of data visualization techniques using R or Python serves as a focal point. Throughout the course, students will develop proficiency in using R Studio for data loading and transformation, explore data dynamics through bar charts, histograms, and box plots, master the art of visualizing data with scatterplots, and ultimately apply these techniques to real-world case studies. By the course's conclusion, students will not only possess a nuanced understanding of data visualization concepts but will also demonstrate the ability to apply these skills effectively in practical scenarios.
CS474: Selected Topics in Data Science
Semester: Spring and Autumn
Offered: 2023/2022
This course aims to develop strong data analytic skills using both theoretical and case-based approaches to apply data mining and advanced statistical techniques to real-world problems facing society. The students will learn about the use of various multivariate methods, how to design the study to collect data amenable for such analysis, and the issues involved in acquiring, storing, accessing, analyzing, and visualizing large, heterogeneous, and real-time data associated with diverse real-world domains.
CS470: Introduction to Data Science
Semester: Spring and Autumn
Offered: 2022/2021/2020
Data science is the study of the generalizable extraction of knowledge from data. Being a data scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases, and other branches of computer science, along with a good understanding of the craft of problem formulation to engineer effective solutions. Students will learn the concepts, techniques, and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.
CS498/CS499: Graduation Projects
Semester: Spring and Autumn
Offered: 2023/2022/2021/2020
The Graduation Projects for computer science students at our institution are the culmination of academic rigor and practical application, structured in two parts to ensure a comprehensive and hands-on learning experience. In Part 1, students leverage the knowledge gained throughout their academic journey, incorporating both theoretical foundations and practical skills acquired in various courses up to Level 10. This initial phase sets the foundation for their problem-solving capabilities and showcases their ability to apply diverse concepts in real-life scenarios. Moving on to Part 2, students delve deeper into addressing real-world challenges, demonstrating an advanced understanding of computer science principles. These projects serve as a testament to their academic growth, technical proficiency, and the seamless integration of theoretical insights with practical problem-solving. The Graduation Projects not only underscore the culmination of their academic journey but also reflect the transformative impact of their education on their ability to contribute meaningfully to the field of Computer Science.
MSIT 604: Cybersecurity Technology and Management (Master's Level)
Semester: Spring and Autumn
Offered: 2023/2022/2021/2020
MSIT 604 is an advanced exploration of Cyber Security, equipping students with cutting-edge knowledge and hands-on skills. Covering topics like Public Key Infrastructure, IT Security Management, Cyber Warfare, and the latest in Satellite Cyber Attack strategies, the course adopts a dynamic blend of traditional and e-learning methods. Evaluation includes quizzes, exams, and a practical mini project/seminar, ensuring students are adept at tackling contemporary cyber threats. Dive into the future of Cyber Security with MSIT 604.
MDS611: Introduction to Data Science (Master's Level)
Semester: Spring and Autumn
Offered: 2023/2022
Businesses, governments, and individuals create massive collections of data as a by-product of their activities. Increasingly, decision-makers and systems rely on intelligent technology to analyze data systematically to improve decision-making. In many cases, automating analytical and decision-making processes is necessary because of the volume of data and the speed with which new data are generated. We will examine how data analysis technologies can be used to improve decision-making. We will study the fundamental principles and techniques of data science, and we will examine real-world examples and cases to place data science techniques in context, develop data-analytic thinking, and illustrate that proper application is as much an art as it is a science. In addition, we will work hands-on with the Python programming language and its associated data analysis libraries.
MDS632: Big Data Applications and Analytics (Master's Level)
Semester: Spring and Autumn
Offered: 2023/2022
In today's world, an enormous amount of data is generated from a variety of sources, which include web server logs, Internet click-stream data, social media content, text from customer emails and survey responses, mobile phone call detail records, and machine data captured by sensors. It has been predicted by experts that it may result in a huge tidal wave of data which is referred to as Big Data. Big data has become so ubiquitous that it has become necessary to have technologies and tools to store, process, and analyze it efficiently. Big data finds applications in several domains including scientific research, government, and industry. Big data analytics is a broad term for the wide range of technologies that help companies make informed business decisions and analyze large volumes of transactional data, as well as other forms of data that may be untapped by more conventional Business Intelligence(BI) programs. This course provides exposure to the real-time use cases of big data and introduces the imperative big data technology, Hadoop. It provides a comparison of Hadoop data processing with other conventional systems. Then we dive deep into the working mechanism of Hadoop focusing on the Hadoop Distributed File System (HDFS) and Map Reduce.
Teaching Philosophy
In every course I teach, my primary objective is to empower my students with a profound understanding of both theoretical and practical aspects. I believe in fostering engagement with academic literature while simultaneously grounding theoretical knowledge in modern computing approaches. My ultimate goal is to educate the engineers and scientists of tomorrow, equipping them with the tools and skills necessary to address real-world challenges across various domains.
My teaching journey has been enriched by three years of experience as an Associate Professor at Majmaah University, where I had the privilege of instructing both undergraduate and graduate students. At the undergraduate level, I taught courses in two distinct tracks: the Artificial Intelligence (AI) track, which included Computer Vision (CS460) and Machine Learning (CS462), and the Data Science track, encompassing Introduction to Data Science (CS470), Data Visualization (CS 473), and Selected Topics for Data Science (CS474). For our master's students, I led courses in Data Science, including Introduction to Data Science (MDS611) and Machine Learning (MDS622), as well as Cyber Security Foundation (MIT604). Throughout these courses, students were encouraged to apply their knowledge through graduating projects and mini-projects.
My teaching philosophy revolves around three core principles: integrating students into the realm of research, nurturing their writing skills, and cultivating systems thinking. I believe in an interdisciplinary and hands-on approach to education, mirroring my research endeavors. In both lectures and seminars, I implement the Socratic method, challenging students to think critically and encouraging active participation. I find immense satisfaction in mentoring and educating students, as it provides a platform for collaborative engagement with the next generation of scholars, which, in turn, broadens my perspectives.
I firmly believe that my experiences in leveraging computer science techniques and expert systems such as Machine Learning and Deep Learning to solve real-life problems will greatly benefit my teaching. These abilities enable me to bridge the gap between complex concepts and student comprehension, making the classroom a space for active learning and idea exploration.
I am enthusiastic about the prospect of teaching a wide range of undergraduate and graduate-level courses. In particular, I aspire to develop a course that explores Machine Learning, applied AI, IoT, and UAVs, with a focus on creating solutions for diverse challenges, including those related to climate, earth, and the environment. My ongoing research in machine learning and deep learning, which has been applied to address critical issues in climate, earth, and environmental science, equips me with the expertise needed to provide students with relevant and cutting-edge knowledge.