Lectures on Software Design and Implementation
I gave these lectures as a part of the course "Software Design and Implementation" at the Department of Computer Science (Institute of Data Science and AI) at University of Aberdeen (2023-24).
Introduction to Software Design
Agile SW Development & DevOps
Software Design Concepts
Design Principles
Design Patterns
Code Refactoring
Dependable Software Systems
Software Security
Component-Level Design
Service-Oriented Architecture (SOA)
Legal Issues in Software Design
Social Issues, Security & Privacy in Software Design
Professional Issues in Software Design
Ethical Issues in Software Design
Lectures on Biostatistics and Health Data Analysis
I gave these lectures as a part of the course "Scientific Reserach Methods" at the Department of Computer Science (Institute of Data Science and AI) at University of Aberdeen (2023-24).
The Scientific Method
Tools for reporting research, including LaTeX
Literature and background research
Experimental Design
Statistical Hypothesis Testing
Probability and Statistics Refresher
Computational Tools for Statistics: Using Python
Nonparametric Statistical Tests and Experimental Design
Parametric Statistical Tests and Experimental Design
Qualitative Methods/Research Metrics
Survival Analysis/Epidemiological Methods
Meta-Analysis and Systematic Reviews
Ethical Issues and Common Mistakes
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]