## 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).

Motivation and Use Cases

Introduction to Artificial Intelligence (AI)

Introduction to Machine Learning (ML)

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).

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).

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).

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).

Introduction to Statistical Learning

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

Unsupervised Learning (Principle Component Analysis, Clustering Methods)

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

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

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

Advanced Video Compression Techniques (AVC H.264 and HEVC H.265)

Please scroll down for lectures on other multimedia topics

## Lectures on Probability and Statistics Programming

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

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).

2. Basic Concepts of Probability

3. Probability Basics: Problem Solving

5. Problem Solving: Discrete Random Variables

6. Continuous Random Variables

7. Probability Distribution in R

8. Joint Probability Distributions

9. Variance and Point Estimation

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).

4. Graphics and Image Data Representations (part 1)

5. Median-cut Algorithm: An Example

6. Graphics and Image Data Representations (part 2)

7. Fundamental Concepts in Video

9. Lossless Compression Algorithms

10. Lossy Compression Algorithms

11. JPEG Standard

12. Introduction to Audio Compression

13. Introduction to Video Compression

14. Multimedia Networks and Applications

## 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

3. Classes, Objects, and Strings

6. Functions and Recursion with Array

7. Class Template Vector and Reviews Pointers

10. Class Construction (Part 2)

11. Class Construction (Part 3)

13. Operator Overloading (Remaining Part)

14. Inheritance

16. Polymorphism

18. Templates

## 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

5. Concepts of Random Process (Part 1)

6. Concepts of Random Process (Part 2)

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

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).

## Lecture Archive

Some older lecture notes are available in this link: https://edustack.weebly.com