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S.M. Riazul Islam, PhD

Lectures on Applied Machine Learning

I gave these lectures as a part of the course "AI Skills Bootcamp​" at the Department of Computer Science at University of Huddersfield (2022 Autumn​ Term).
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Motivation and Use Cases
Introduction to Artificial Intelligence (AI)
Introduction to Machine Learning (ML)
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Algorithms, Applications, and Hands-on:

Linear and Logistic Regression
Decision Tree and Random Forest
Naive Bayes and Support Vector Machine (SVM)
Dimensionality Reduction, KNN, and Gradient Boosting
Neural Network (NN) and Recurrent Neural Network (RNN)
K-Means and t-SNE
Data Preparation Techniques
Feature Extraction Techniques
Autoencoders and Linear Discriminant Analysis (LDA)
Validation and Testing
​Building E-mail Spam Filter using Machine Learning

​Lectures on Inventory Control with Machine Learning

I gave these lectures as a part of the course "AI Skills Bootcamp​" at the Department of Computer Science at University of Huddersfield (2022 Autumn​ Term).
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​Introduction and Motivation
Inventory Tracking
Inventory Control
Stock Prediction
Introduction to Time Series Data
Statistical Methods for Time Series Forecasting (Part 1)
Statistical Methods for Time Series Forecasting (Part 2)
Machine Learning for Time Series Forecasting Part 1
Machine Learning for Time Series Forecasting Part 2
Python Packages for Time Series Analysis and Forecast
Inventory Management Software Architecture
Inventory Planning & Optimization
Predicting Back-Orders
​New Paradigms in Inventory Management

Lectures on Data Analysis

I gave these lectures as a part of the module "Data Analysis Introduction" at the Department of Computer Science at University of Huddersfield (2022 Autumn Term​).
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Introduction to Data Analysis
Real World Examples of Data Analysis & Applications
Basic Statistical Concepts
Measures of Central Tendencies (Part 1)
Measures of Central Tendencies (Part 2)
Data Visualization and Data Design (Part 1)
Data Visualization and Data Design (Part 2)
Data Source: Finding Data in Real World
Introduction to Dashboards
Alternative data analytics tool - Python

Lectures on Inferential Statistics

I gave these lectures as a part of the course "Probability and Statistics" at the Department of Computer Science and Engineering at Sejong University (2022 Spring Semester).
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Probability and Random Variables
Probability Distributions (Continuous and Discrete)
Expectation and Variance
Introduction to Estimation
Confidence Interval
Test of Hypothesis Based on a Single Sample
Test of Hypothesis Based on Two Samples
Single-Factor Analysis of Variance (ANOVA)
Multi-Factor Analysis of Variance (ANOVA)
Goodness of Fit Tests

​Python Codes for Inferential Statistics
[Will be uploaded here]

Lectures on Statistical Learning

I gave these lectures as a part of the course "Introduction to Statistical Learning" at the Department of Computer Science and Engineering at Sejong University (2021/09-2021/12).
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Introduction to Statistical Learning
Linear Regression
Linear Regression Review
Classification (Logistic Regression)
Classification (Generative Models)
Resampling (Cross-validation, The Bootstrap)
Model Selection and Regularization (Subset Selection, Lasso and Ridge Regression)

Model Selection and Regularization (Dimension Reduction Methods)
Tree-Based Methods: Decision Tree Basics
Tree-Based Methods: Ensemble Learning
Support Vector Machine (SVM)
Unsupervised Learning (Principle Component Analysis, Clustering Methods)

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Python Codes for Statistical Learning

[Python Basics] [Linear Regression] [Regression Review] [Classification] [Model Selection and Regularization] [Ensemble Learning] [SVM and Clustering]

​Lectures on Multimedia

I gave these lectures as a part of the course "Multimedia" at the Department of Computer Science and Engineering at Sejong University (2021/09-2021/12).

Data Visualization: Introduction
Data Visualization: Design
Multimedia Communications and Networks

Content Distribution, Social Media, and Cloud Computing
AR, VR, and Principles of Animation
Introduction to Generative Adversarial Network (GAN)

Please scroll down for lectures on other multimedia topics

Lectures on Image Processing

I gave these lectures as a part of the course "Image Processing" at the Department of Computer Science and Engineering at Sejong University (2020/1).

Introduction to Image Processing
Digital Image Fundamentals
Intensity Transformations and Spatial Filtering (Part 1)
Intensity Transformations and Spatial Filtering (Part 2)
Filtering in the Frequency Domain (Part 1)
Filtering in the Frequency Domain (Part 2)
Image Restoration and Reconstruction
Image Compression
Image Segmentation
Feature Extraction

Lectures on Multimedia

I gave these lectures as a part of the course "Multimedia" at the Department of Computer Science and Engineering at Sejong University (2019/2).

Multimedia Frameworks and Tools
Video Compression Principles
​Advanced Video Compression Techniques (AVC H.264 and HEVC H.265)

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Please scroll down for lectures on other multimedia topics

Lectures on Probability and Statistics Programming with Python

I gave these lectures as a part of the course "Probability and Statistics Programming" at the Department of Computer Science and Engineering at Sejong University (2019/1).

Statistics with Python Practice 1
Statistics with Python Practice 2
​Statistics with Python Practice 3​
​Statistics with Python Practice 4
​Statistics with Python Practice 5

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Please scroll down for lectures on other Probability and Statistics Programming topics

Lectures on Image Processing

I gave these lectures as a part of the course "Image Processing" at the Department of Computer Science and Engineering at Sejong University (2019/1).

Lectures Contents: TBA

Lectures on Multimedia

I gave these lectures as a part of the course "Multimedia" at the Department of Computer Science and Engineering at Sejong University (2018/2).

Lectures Contents: TBA

Lectures on Probability and Statistics Programming in R

I gave these lectures as a part of the course "Probability and Statistics Programming" at the Department of Computer Science and Engineering at Sejong University (2018/1).

1. Course Information
2. Basic Concepts of Probability
3. Probability Basics: Problem Solving
4. Discrete Random Variables
5. Problem Solving: Discrete Random Variables
6. Continuous Random Variables
​7. Probability Distribution in R
8. Joint Probability Distributions
9. Variance and Point Estimation
10. Confidence Intervals
11. Test of Hypotheses
12. Hypotheses Testing in R
13. Analysis of Variance (ANOVA)
​14.​​ Simple Linear Regression

Lectures on Internet of Things (IoT)

I gave these lectures as a part of the course "Internet of Things: Protocols and Applications" at the Department of Computer Science and Engineering at Sejong University (2018/1).

Lectures Contents: TBA

Lectures on Multimedia

I gave these lectures as a part of the course "Multimedia" at the Department of Computer Science and Engineering at Sejong University (2017/2).

1. Course Information
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2. Introduction to Multimedia
3. Multimedia Authoring
4. Graphics and Image Data Representations (part 1)
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5. Median-cut Algorithm: An Example
6. Graphics and Image Data Representations (part 2)
7. Fundamental Concepts in Video
​8. Basics of Digital Audio
9. Lossless Compression Algorithms
10. Lossy Compression Algorithms​
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11. JPEG Standard
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12. Introduction to Audio Compression
13. Introduction to Video Compression
14. Multimedia Networks and Applications
15. Multimedia Retrieval
  • Assessment Rubrics
Assignments
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PA-1 PA-2 PA-3 PA-4 PA-5 PA-6 PA-7 PA-8 PA-9 PA-10
EA-1 EA-2

Lectures on Object Oriented Programming (OOP) in C++

I gave these lectures as a part of the course "Problem Solving and Lab: C++" at the Department of Computer Science and Engineering at Sejong University (2017/1).

1. Introduction to C++ and OOP
2. I/O and Operators
3. Classes, Objects, and Strings
4. Control Statements
5. Functions and Recursion
6. Functions and Recursion with Array
7. Class Template Vector and Reviews Pointers
8. Reviews Pointers (Part 2)
9. Class Construction
​10. Class Construction (Part 2)
​11. Class Construction (Part 3)
12. Operator Overloading
13. Operator Overloading (Remaining Part)
14. Inheritance
15. Inheritance (Part 2)
16. Polymorphism
17. Polymorphism (Part 2)
18. Templates
19. Templates (Part 2)
​20. Stream Input/Output
21. Exception Handling
​22. Standard Template Library

Lectures on Random Process

I gave these lectures as a part of the course "Random Process" at the Department of Information and Communication Engineering at Inha University, South Korea

1. Basic Concepts of Probability Theory
2. Overview of Random Variables
3. Overview of Multiple Random Variables
4. Sum of Random Variables
5. Concepts of Random Process (Part 1)
6. Concepts of Random Process (Part 2)
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7. Concepts of Random Process (Part 3)
8. Analysis and Processing of Random Signals (Part 1)

9. Analysis and Processing of Random Signals (Part 2)
10. Markov Chains
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11. Queueing Theory

Lectures on Wireless Communications

I gave these lecture as a part of the course "Advanced Data Communications and Wireless Networks" at the Department of CSE at Ahsanullah University of Science and Technology (AUST), Bangladesh (2013 and 2014).
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  • Basics of Wireless Communications
  • Satellite Communications 1
  • Satellite Communications 2
  • Cellular Networks and GSM Architecture
  • CDMA Systems
  • Wireless LAN and Wireless PAN
  • Bluetooth Technology
  • Antenna Types

Courses I Taught @University of Dhaka 

APECE-302: Radio & Television Engineering (Grade: Junior, Session: 2012-13)
[Course Home]

APECE-402: Microwave & Satellite Communication
 (Grade: Senior, Session: 2011-12)
[Course Home] [Lectures/Notices]

APECE-302: Radio & Television Engineering (Grade: Junior, Session: 2011-12)
[Course Home] [Lectures/Notices]

EEE-1222: Basic Electronics (Grade: Freshman (2), Session: 2011-12)
[Course Home] [Lectures/Notices]

APECE-308: Microprocessor & Assembly Language Programming (2006, 2007)

APECE-203: Electrical Machines & Measurements (2006)

APECE-406: Material Science (2006, 2007)

APECE-205: Physical Electronics & Electronic Devices (2007)

Others Courses I Teach/Taught

  • Advanced Data Communications and Wireless Networks
  • Advanced Electronics
  • Wireless Sensor Networks
  • Power Electronics
  • Digital Signal Processing
  • Data Structure
  • Computer Programming in C
  • Microwave Engineering
  • Digital Modulation Techniques
  • Fundamental of Physics
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