UNITED INTERNATIONAL UNIVERSITY
Department of Computer Science and Engineering (CSE)
Course Syllabus
1 Course Title Deep Learning
2 Course Code CSE 6211
3 Trimester and Spring 2025
Year
4 Pre-requisites N/A
5 Credit Hours 3.00
6 Section M
7 Class Hours Saturday: 2:30 PM - 05:00 PM
8 Class Room 410
9 Instructor’s Name Dr. Sumaiya Tabassum Nimi
10 Email sumaiya@cse.uiu.ac.bd
11 Office Room # 318 B
12 Counseling Hours
Saturday 10-11AM, 12:31-2:00PM
13 Textbooks
1. Learning Deep Learning, Magnus Ekman, NVIDIA, 2021 (Web:
ldlbook.com)
2. Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron
Courville, MIT Press, 2016 (Web: deeplearningbook.org)
14 Reference Dive into Deep Learning, Zhang, Lipton, Li, and Smola (Web: d2l.ai)
Conference Papers, Online research articles etc.
15 Course Contents Introduction to Deep Learning, Historical Context, and Parametric Machine
Learning Algorithms; Introduction to Classification, Classifiers,
Regularization, and Optimization for Performance; Neural Networks, Basic
Linear Algebra, and Backpropagation; Deep Learning Model Training: Data
Preprocessing, and Augmentation, Activation Functions, and Regularization,
Hyperparameter Tuning and Model Ensembles.
Deep Learning for Computer Vision (Convolutional Neural Networks):
Convolution, Pooling, and Popular CNN Architectures; Deep Learning
Tricks and Trades: Transfer Learning, and Neural Architecture Search;
Introduction to Natural Language Processing using Deep Learning Models;
Advanced Computer Vision Using Deep Learning; Advanced Natural
Language Processing Using Deep Learning
20 Lecture Outlines
Lecture
Topics Topics/Assignments Reading Reference
Outcomes/Activities
Introduction to Deep Learning, Slides, Books, Lecture
1 Historical Context, and Parametric
Research Papers Discussion
Machine Learning Algorithms
Introduction to Classification, Slides, Books,
2 Lecture
Classifiers, Regularization, and
Research Papers Discussion
Optimization for Performance
Neural Networks, Basic Linear Slides, Books, Lecture
3
Algebra, and Backpropagation Research Papers Discussion
Deep Learning Model Training: Data Slides, Books,
4 Preprocessing, and Augmentation, Research Papers Lecture
Activation Functions, and Paper Presentation
Regularization
Deep Learning Model Training: Slides, Books, Lecture
5 Hyperparameter Tuning and Model Research Papers
Paper Presentation
Ensembles
Deep Learning for Computer Vision Slides, Books,
6 (CNN Models): Convolution, Research Papers Lecture
Pooling, and Popular CNN Paper Presentation
Architectures
Mid Weeks
Deep Learning Tricks and Trades: Slides, Books,
7 Lecture
Transfer Learning, and Neural Research Papers
Architecture Search Paper Presentation
Introduction to Natural Language Slides, Books,
8 Lecture
Processing using Deep Learning Research Papers
Models Paper Presentation
Advanced Computer Vision Using Slides, Books, Lecture
9 Research Papers
Deep Learning Paper Presentation
Advanced Natural Language Slides, Books, Lecture
10 Research Papers
Processing Using Deep Learning Paper Presentation
Research Paper,
11 Term Paper writing and Presentation Tools, Technical Presentation
Reports, etc.
Research Paper,
12 Term Paper Finalization and
Tools, Technical Presentation
Presentation
Reports, etc.
Review Class
Semester Final Exam Week
Appendix 1: Assessment Methods
Assessment Types Marks
Attendance 5%
Assignment 10%
Paper Presentation 15%
Term Paper 30%
Final Exam 40%
Appendix 2: Grading Policy
Letter Grade Marks % Grade Point Letter Grade Marks% Grade Point
A (Plain) 90-100 4.00 C+ (Plus) 70-73 2.33
A- (Minus) 86-89 3.67 C (Plain) 66-69 2.00
B+ (Plus) 82-85 3.33 C- (Minus) 62-65 1.67
B (Plain) 78-81 3.00 D+ (Plus) 58-61 1.33
B- (Minus) 74-77 2.67 D (Plain) 55-57 1.00
F (Fail) <55 0.00
