CSE-403 Machine Learning



E-mail: atik@cse.green.edu.bd

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:office: Room: A-510 Desk No. : 08

Class Routine – Spring 2026 Semester


Day 08:30-10:00 10:00-11:30 11:30-1:00 Break 1:30-03:00 3:00-4:30    
Sat                
Sun CSE 404
231_D1
K-109		 CSE 404
231_D1
K-109		 CSE 403
231_D1
J-107		   Tutor Time Tutor Time    
Mon Tutor Time Tutor Time            
Tue   GED-103
252_D1
K-102		 CSE 403
231_D1
J-105		   CSE 404
231_D3
K-101		 CSE 404
231_D3
K-101		    
Wed   GED-103
252_D1
G-101		            
Fri                



Topic Outline

Topic Outline

Lecture Selected Topic Article / Materials Problems
(1–2) Introduction to Machine Learning; What is ML? Three Approaches: Supervised, Unsupervised, Reinforcement Learning Class Notes, Slides  
(3–4) Elements of a Supervised Learning Problem; Dataset and Learning Algorithm Overview Class Notes, Slides Assignment 1
(5–6) Linear Regression: Concepts, Gradient Descent, Ordinary Least Squares Class Notes, Slides, Math Notes  
(7–9) Classification: Classification Basics, Logistic Regression, Softmax Regression, Multi-Class Classification Class Notes, Slides, Math Notes  
(10–11) K-Nearest Neighbors, Naive Bayes Classifier Class Notes, Slides, Math Notes Quiz 1
(12–13) Support Vector Machines (Linear & Nonlinear), Decision Trees (ID3) Class Notes, Slides, Math Notes  
  Midterm Examination    
(14–15) Regularization & Model Evaluation: L1/L2 Regularization, Overfitting, Bias-Variance, Metrics, Cross-Validation, Bootstrap Class Notes, Slides, Math Notes  
(16) Ensemble Methods: Bagging, Boosting, Random Forests Class Notes, Slides Call for a Group Project
(17–18) Neural Networks: Neuron Model, Activation Functions, Network Architecture, Forward Propagation Class Notes, Slides, Math Notes  
(19–20) Loss Functions, Backpropagation, Gradient Descent, Initialization, Normalization, Vanishing/Exploding Gradients Class Notes, Slides, Math Notes Quiz 2
(21) Neural Network Regularization (Dropout, Weight Decay), Optimization (SGD, Adam, RMSprop), Hyperparameter Tuning, Model Evaluation Class Notes, Slides, Math Notes  
(22) Convolutional Neural Networks (CNNs): Convolution, Filters, Layers, Feature Maps, Pooling, Architecture Design Class Notes, Slides, Math Notes  
(23) CNNs: Forward Propagation, Loss Functions, Backpropagation, Popular Architectures (VGG, ResNet) Class Notes, Slides, Math Notes  
(24) CNNs with Attention Mechanisms (SE Block, CBAM Overview), Transfer Learning, Fine-Tuning, Model Visualization Class Notes, Slides  
(25) Generative Models: Autoencoders, VAE, GANs, Conditional/Modern Generative Models, Applications, Evaluation, Ethics, Course Wrap-Up Class Notes, Slides, Math Notes  
  Final Examination   Â