| Course Code |
CSC363 |
| Course Title |
Artificial Intelligence |
| Credit Hours |
3+1 |
| Prerequisites by Course(s) and Topics |
Data Structures and Algorithms |
| 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 umer Farooq/Arfa Hassan |
| URL (if any) |
- |
| Current Catalog Description |
- |
| Textbook (or Laboratory Manual for Laboratory Courses) |
● Artificial Intelligence Lab Manual ● Python for Everybody: Exploring Data in Python 3 by Charles Severance. |
| Reference Material |
● Mathworks MATLAB Tutorials |
| Course Goals |
● Understand key components in the field of artificial intelligence. ● Implement classical artificial intelligence techniques. ● Analyze artificial intelligence techniques for practical problem solving. |
| 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 |
Introduction to AI |
|
2 |
AI tools and techniques |
| Week 2 |
3 |
introduction to matlab |
|
4 |
Datasets in AI |
| Week 3 |
5 |
Dataset preprocessing |
|
6 |
Feature extraction |
| Week 4 |
7 |
Pattern recognition neural network |
|
8 |
Model Testing |
| Week 5 |
9 |
Classifier Learner |
|
10 |
Introduction to fuzzy logic |
| Week 6 |
11 |
Type I Fuzzy |
|
12 |
Type II Fuzzy |
| Week 7 |
13 |
Neuro Fuzzy |
|
14 |
Genetic Algorithm |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Python zero I |
|
16 |
Python zero II |
| Week 10 |
17 |
File handling with python |
|
18 |
Csv, Os , panda Matplotlib |
| Week 11 |
19 |
Kearas, Sklearn , Panda , tensor flow , open cv , yolo |
|
20 |
Data preprocessing |
| Week 12 |
21 |
feature extraction with python |
|
22 |
Machine Learning model Training I |
| Week 13 |
23 |
Machine Learning model Training II |
|
24 |
Model Testing |
| Week 14 |
25 |
Introduction to deep learning |
|
26 |
Deep learning model Training |
| Week 15 |
27 |
Deep learning model evolution measures |
|
28 |
Yolo, darknet |
| Week 16 |
29 |
Front end design of AI base project I |
|
30 |
Front end design of AI base project II |
| Week 17 |
2 hours |
Final Term |
|
| Laboratory Projects/Experiments Done in the Course |
|
| Programming Assignments Done in the Course |
|
