Introduction to Neural Networks and Deep Learning

EEL6812 (Spring 2026)
Department of ECE
University of Central Florida
Orlando, FL, USA

Instructor and TA information

Email: shahana.ibrahim@ucf.edu
Class Hours: Monday and Wednesday 1:30-2.45PM
Class Location: CB1 O218
Office Hours: Monday and Wednesday 3-4:30PM
Office Location: UCF Global 224

Email: tarhibal.azad@ucf.edu
Office hours: TBD

Description

This course introduces the basics of neural networks and deep learning in a rigorous way. The aim is to open doors to the vast world of deep learning, understand its strengths, challenges, potential risks, usefulness to the humankind for a wide spectrum of applications. In addition, the students are expected to grasp the fundamentals in designing a deep learning model and learn to adapt it for their own research purposes. The course materials would be designed accordingly such that deep learning research in multidisciplinary domains are benefitted.

Reference Textbooks

Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. An MIT Press book. 2016
Charu C. Aggarwal. Neural Networks and Deep Learning: A Textbook. Springer. 2019
Dive into Deep Learning

Grading

Assignments 30%, Quiz 10%, Final Project 60%.

Lecture Schedule

Lecture slides will be posted on the course website before each lecture. The instructor reserves the right to alter this schedule as needed.

# Week	Dates	Lecture Topics	Lecture Notes	Resources	Homeworks	Quiz	Project
1	Jan 12	Introduction A brief history of deep learning	Introduction to Deep Learning	A Logical Calculus of the Ideas Immanent in Nervous Activity Perceptron Parallel Distributed Processing LeCun’s seminal paper on CNN
	Jan 14	Linear algebra basics Probability basics	Basics-part 1	Scalar, vectors, arrays, tensors with Python Linear algebra fundamental with Python Probability fundamentals with Python
2	Jan 21	Machine learning basics	Basics-part 2	Simple linear regression model and its optimization Chapter 5, Machine Learning Basics, Deep Learning Book		Quiz 1
3	Jan 26, 28	Neural Network Optimization I			HW1 Out	Quiz 2	Project Issue
4	Feb 2, 4	Neural Network Optimization II				Quiz 3
5	Feb 9, 11	Convolutional Neural Networks I			HW1 Due, HW2 Out	Quiz 4
6	Feb 16, 18	Convolutional Neural Networks II				Quiz 5
7	Feb 23, 25	Recurrent Neural Networks I			HW2 Due	Quiz 6
8	Mar 2, 4	Recurrent Neural Networks II			HW3 Out	Quiz 7	Project Idea Submission
9	Mar 9, 11	Graph Neural Networks				Quiz 8
10	Mar 16, 18	Spring Break
11	Mar 23, 25	Generative Models I			HW3 Due	Quiz 9
12	Mar 30, Apr 1	Generative Models II				Quiz 10
13	Apr 6, 8	Transformers
14	Apr 13, 15	Project Presentations					Proposal Presentations
15	Apr 20, 22	Project Presentations
16	Apr 27	Foundation Models (LLMs, VLMs)
	May 1						Project Report
	May 8						Grades Due

Jan 19 is a federal holiday; Mar 16-20 is Spring Break

Shahana Ibrahim