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Programming Courses
I am very passionate about programming and enjoy solving problems a lot. From a young student who once feared programming, I mastered programming through a lot of effort. Now as a teacher, I find great fulfillment in encouraging my students to overcome their programming fears and approach the subject with passion.
Anyone who wants to learn programming can benefit from the following courses I've created.
Course contents, solved problems, and assignments can be found here: GitHub
Recorded lectures: YouTube Playlist
Learn Python Basics
Course contents, solved problems, and assignments can be found: GitHub
Recorded lectures: YouTube Playlist
Course contents, solved problems, and assignments can all be found here: GitHub
Recorded lectures: YouTube Playlist
Courses
The courses I instructed in the Summer - 2023 semester
EEE 4871: Biomedical Signal Processing
EEE 4872: Biomedical Signal Processing Lab (https://github.com/newaz-aa/Biomedical_Image_Processing)
EEE 4602: Signals and Systems Lab (https://github.com/newaz-aa/Signal_Processing_Lab)
The courses I instructed in the Winter - 2023 semester
EEE 4709: Artificial Intelligence and Machine Learning (Course Outline)
EEE 4710: Artificial Intelligence and Machine Learning Lab (https://github.com/newaz-aa/Hands-on-Machine-Learning-with-Scikit-Learn-and-TensorFlow)
EEE 4518: Electrical and Electronic Workshop
The courses I instructed in previous semesters
EEE 4307: Digital Logic Design
Phy 4421: Semiconductor Physics
Math 4522: Numerical Methods Lab (Course materials - https://github.com/newaz-aa/Numerical_methods_lab)
EEE 4705: Control System Engineering Lab ( Course materials - https://github.com/newaz-aa/Control-System-Engineering-Lab-Manual)
Thesis and Capstone Project
I have guided several groups of students in their undergraduate thesis and capstone project.
Group - 01: Classification of rice varieties using CNN (2024- cont.)
Group - 02: Learning from Imbalanced Data (2023-24)
Group - 03: Customer Churn Prediction & Factors Identification Using Data Mining Techniques (2022-23)
Customer Churn Prediction
Customer churn poses a significant challenge for business enterprises. In this project, we worked with two churn datasets: one from IBM Watson Cognos Analytics and the other from BigML. We analyzed the datasets to identify the major factors responsible for churn customers. Moreover, we segmented the churn customers through unsupervised learning techniques such as clustering to gain insights regarding their group-wise behavior and tendencies for targeted retention strategies.
Learning from Imbalanced Data
Predictive modeling frequently faces class imbalance, especially in the medical field. This thesis aims to thoroughly evaluate the performance of various advanced methods on different imbalanced datasets, highlighting the strengths and weaknesses of each approach. A hybridization between sampling and cost-sensitive learning is also proposed and implemented in the prediction of complications from myocardial infarction (MI). The use of DL techniques for imbalanced learning tasks has also been investigated.
G11-Thesis-ppt.pptx"ae boe i le eliathon, im tulithon"
... Lady Galadriel
If you should ever need my help, I will come
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