Course Description

COURSE DESCRIPTION

NAME OF INSTITUTION	Lahore Garrison University
PROGRAM (S) TO BE EVALUATED	Computer Science , Spring 2023

Course Description : Students learn how to use application program interfaces (APIs), such as TensorFlow and Keras, for building a variety of deep neural networks: convolutional neural network (CNN), recurrent neural network (RNN), self-organizing maps (SOM), generative adversarial network (GANs), and long short-term memory (LSTM).

Course Code

CSC390

Course Title

Deep Learning

Credit Hours

3+0

Prerequisites by Course(s) and Topics

Machine Learning

Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.)

SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%

Course Coordinator

Miss Unaiza Tallal

URL (if any)

Current Catalog Description

Textbook (or Laboratory Manual for Laboratory Courses)

Deep Learning, 1st Edition, Yoshua Bengio, Ian Goodfellow, Aaron Courville, Neural networks and deep learning, 1st Edition, Michael A. Nielsen

Reference Material

Hands‑On Machine Learning with Scikit‑Learn and Tensor Flow, 1st Edition, Aurélien Géron

Course Goals

“The main objective of this course is to make students comfortable with tools and techniques required in handling large amounts of datasets. They will also uncover various deep learning methods in NLP, Neural Networks”.

Course Learning Outcomes (CLOs):

At the end of the course the students will be able to:	Domain	BT Level*
Explain the basic ideologies behind neural networks and deep learning	Cognitive domain	2
Measure modeling aspects of various neural network architectures	Psychomotor domain	4
Build a deep learning model for the given dataset to solve a particular problem.	Psychomotor domain	5
* BT= Bloom’s Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective domain

Topics Covered in the Course, with Number of Lectures on Each Topic (assume 15-week instruction and one-hour lectures)

Week	Lecture	Topics Covered
Week 1	1
	2
Week 2	3
	4
Week 3	5
	6
Week 4	7
	8
Week 5	9
	10
Week 6	11
	12
Week 7	13
	14
Week 8	1 hours	Mid Term
Week 9	15
	16
Week 10	17
	18
Week 11	19
	20
Week 12	21
	22
Week 13	23
	24
Week 14	25
	26
Week 15	27
	28
Week 16	29
	30
Week 17	2 hours	Final Term

Laboratory Projects/Experiments Done in the Course

Programming Assignments Done in the Course

Instructor Name	Miss Unaiza Tallal
Instructor Signature
Date