| Course Code |
CSC |
| Course Title |
Advance Deep Learning |
| Credit Hours |
3 |
| 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 |
Dr Areej Fatima |
| URL (if any) |
https://classroom.google.com/u/7/c/NTU4NzA1NjI1Mzc3 |
| Current Catalog Description |
|
| Textbook (or Laboratory Manual for Laboratory Courses) |
• Deep Learning for Coders with Fastai and PyTorch writer by Jeremy Howard. |
| Reference Material |
• Deep Learning writer by Josh Patterson. Other Resources/Reference Books • Hands-On Machine Learning with Google-Colab • Artificial Intelligence • Uploaded Material in Google Class |
| Course Goals |
This course covers a broad range of topics in deep learning and other types of machine learning algorithms. This course explains about the fundamental methods involved in deep learning, including the underlying optimization concepts (gradient descent and backpropagation), typical modules they consist of, and how they can be combined to solve real-world problems. While interacting with the students, it familiarizes students with the concepts of Convolutional Neural Network, GAN, Federated Learning and Explainable Artificial Intelligence going on within each layer that Make students confident that they can solve machine learning problems, using deep learning Techniques |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| * 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 |
|
