CSE-403 Machine Learning
E-mail: atik@cse.green.edu.bd
☎ Mob. +8801912961096
Room: A-510 Desk No.: 08
Class Routine – Fall 2026 Semester
| Day | 08:30-10:00 | 10:00-11:30 | 11:30-13:00 | Break | 13:30-15:00 | 15:00-16:30 | 16:30-18:00 | 18:00-19:30 |
|---|---|---|---|---|---|---|---|---|
| Sat | Research Time | Research Time | ||||||
| Sun | Tutor Time | Tutor Time | CSE 401 232_D2 A-602 | |||||
| Mon | Tutor Time | CSE 403 241_D1 B-206 | Weekly Academic Committee Meeting (WACM) | Weekly Academic Committee Meeting (WACM) | ||||
| Tue | CSE 401 232_D2 K-103 | Tutor Time | Tutor Time | |||||
| Wed | CSE 404 241_D2 A-502 | CSE 404 241_D2 A-502 | CSE 403 241_D1 A-603 | CSE 404 241_D3 A-501 | CSE 404 241_D3 A-501 |
Topic Outline
| Lecture | Selected Topic | Article / Materials | Problems |
|---|---|---|---|
| (1-2) | Introduction to Machine Learning; What is ML? Three Approaches: Supervised, Unsupervised, Reinforcement Learning | Slides | |
| (3-4) | Elements of a Supervised Learning Problem; Dataset and Learning Algorithm Overview | Slides | |
| (5-6) | Linear Regression: Concepts, Gradient Descent, Ordinary Least Squares, Regularization & Model Evaluation | Slides, Math Notes, Math Notes | CT 1 |
| (7-9) | Classification: Classification Basics, Logistic Regression, Softmax Regression, Multi-Class Classification, Regularization & Model Evaluation | Slides, Math Notes, Math Notes | Call for Paper Presentations |
| (10-11) | K-Nearest Neighbors, Naive Bayes Classifier | Slides, Math Notes, Math Notes | |
| (12-13) | Support Vector Machines (Linear & Nonlinear), Decision Trees (ID3) | Slides, Math Notes, Math Notes | Assignment 1 |
| Midterm Examination | |||
| (14-15) | Neural Networks: Neuron Model, Activation Functions, Network Architecture, Forward Propagation | Slides, Math Notes | |
| (16-18) | Loss Functions, Backpropagation, Gradient Descent, Initialization, Normalization, Vanishing/Exploding Gradients | Slides, Math Notes | |
| (19) | Neural Network Regularization (Dropout, Weight Decay), Optimization (SGD, Adam, RMSprop), Hyperparameter Tuning, Model Evaluation | Slides, Math Notes | CT 2 |
| (20) | Convolutional Neural Networks (CNNs): Convolution, Filters, Layers, Feature Maps, Pooling, Architecture Design | Slides, Math Notes | |
| (21) | CNNs: Forward Propagation, Loss Functions, Backpropagation, Popular Architectures (VGG, ResNet) | Slides, Math Notes | |
| (22) | CNNs with Attention Mechanisms (SE Block, CBAM Overview), Transfer Learning, Fine-Tuning, Model Visualization | Slides, Math Notes | |
| (23) | Cross-Validation, Bootstrap | Slides, Math Notes | |
| (24) | Ensemble Methods: Bagging, Boosting, Random Forests | Slides, Math Notes | |
| (25) | Generative Models: Autoencoders, VAE, GANs, Conditional/Modern Generative Models, Applications, Evaluation, Ethics, Course Wrap-Up | Slides | |
| Final Examination |