Course Code |
CSC363 |
Course Title |
Artificial Intelligence |
Credit Hours |
3+1 |
Prerequisites by Course(s) and Topics |
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Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.) |
SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%
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Course Coordinator |
Arfa Hassan |
URL (if any) |
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Current Catalog Description |
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Textbook (or Laboratory Manual for Laboratory Courses) |
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Reference Material |
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Course Goals |
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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 |
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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 |
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2 |
AI Tools and Techniques |
Week 2 |
3 |
Introduction to matlab |
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4 |
Datasets in AI |
Week 3 |
5 |
Data preprocessing |
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6 |
Feature Extraction |
Week 4 |
7 |
Pattern Recognition Neural Network |
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8 |
Model Testing |
Week 5 |
9 |
classifier learner |
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10 |
introduction to fuzzy logic |
Week 6 |
11 |
Type I Fuzzy |
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12 |
Type II fuzzy |
Week 7 |
13 |
Neuro fuzzy |
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14 |
Genetic Algorithm |
Week 8 |
1 hours |
Mid Term |
Week 9 |
15 |
python zero I |
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16 |
Python zero II |
Week 10 |
17 |
File Handling with Python |
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18 |
Uninformed Search |
Week 11 |
19 |
informed Search |
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20 |
CSV, os , pandas , matplotlib |
Week 12 |
21 |
Kearas, Sklearn |
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22 |
Tensor flow , open cv |
Week 13 |
23 |
EDA part I |
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24 |
EDA part II |
Week 14 |
25 |
Machine Learning part I |
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26 |
Machine Learning part II |
Week 15 |
27 |
Introduction to Deep Learning |
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28 |
Deep Learning part I |
Week 16 |
29 |
Yolo , Darknet |
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30 |
Front end Design of AI Base Project I |
Week 17 |
2 hours |
Final Term |
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Laboratory Projects/Experiments Done in the Course |
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Programming Assignments Done in the Course |
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