EE5438 Applied Deep Learning

Course Description

In this hands-on course, Applied Deep Learning, you will delve into the practical applications of deep neural networks, exploring both the theoretical foundations and real-world implementations of cutting-edge AI models. Through a combination of lectures and Python-based group projects utilizing frameworks like PyTorch, you will gain the essential skills to design, train, fine-tune, and deploy deep learning architectures to solve complex problems in AI. By the end of the course, you will be equipped to apply state-of-the-art deep learning techniques to build innovative solutions and tackle real-world challenges.

Topics Covered

• A Brief History of AI with Deep Learning
• Perceptron and Multi-Layer Perceptrons (MLPs)
• Gradient Descent and Backpropagation
• Optimizations and Regularizations
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Self-Attention and Transformers
• Large Language Models (LLMs): BERT, GPT and T5
• LLM Decoding Strategies and Prompt Engineering
• Parameter-Efficient Fine-Tuning (PEFT)
• Instruction Tuning and Preference Alignment
• Multimodality: CLIP, Flamingo, LLaVA, GPT-4V

Grading

• Assignments: 15%
• Quiz: 5%
• Midterm: 10%
• Group Project: 20%
• Final Exam: 50%

Prerequisites

• Multi-variable Calculus and Linear Algebra
• Probability and Statistics
• Object-Oriented Programming

Please be advised: EE5438 is a course with STRONG Mathematical and Programming components.

Instructor

Prof. PO Lai-Man
Email: eelmpo@cityu.edu.hk
Phone: 3442-7779
Room: G6506, Green Zone, 6/F, AC1

Graders

• JIANG Zeyu - zeyujiang4-c@my.cityu.edu.hk
• LI Kun - kunli25-c@my.cityu.edu.hk
• LIU Yuyang - yuyangliu5-c@my.cityu.edu.hk
• WU Haoxuan - haoxuanwu2-c@my.cityu.edu.hk

Course Details

• Level and Units: P5 Level with 3 CUs
• Lecture: Thursday 9:00am to 11:50am
• Venue: YEUNG LT-14

Important Rules

Lecture Attendance

Required. You are responsible for whatever is taught in the lecture and tutorial.

Submission Guidelines

• Format: Hardcopies are not required
• Delivery: Soft copies (PDF, PPT, Colab source code, related files) must be uploaded to CANVAS by 11:00pm deadline
• Late Submission: Must be received by course email before 11:00PM on due date
• Policy: NO late homework accepted without previous arrangement with instructor
• Penalty: If approved, late homework receives 20% per business day penalty
• Format Requirements: Write name and student number on top right corner, show all work clearly

Academic Integrity

Cheating: Copying fellow classmates' assignments or programs is a very serious offense! If found cheating, you will automatically get an F grade in this course and your act will be reported to the Department for necessary disciplinary actions. Please don't let others copy your assignments or programs as we don't have a way to tell who is copying who and you may be liable to the penalties.

Quick Links

• View the course schedule
• Check out project assignments
• Browse helpful resources and links

Department Information

Department of Electrical Engineering
City University of Hong Kong
Tat Chee Avenue, Kowloon Tong, Hong Kong
Visit Department Website