This course offers a gentle introduction to the concepts and methodologies in Artificial Intelligence from both theoretical and practical perspectives. This includes designing intelligent agents through techniques such as state-space search methods, knowledge representation and inference, and Machine Learning. By the end of this course, students are expected to have the ability to develop intelligent solutions for semi-real world problems using the appropriate methods discussed in the course. Furthermore, they are expected to gain knowledge and experience in analyzing the inner workings of these methods and customizing them for specific problems.
Please contact us through the Telegram channels at https://t.me/ai_fall_2024_rohban and https://t.me/ai_fall_2024_fereydooni. For necessary cases, email the respective heads of teaching assistance.(Ensure that you use a proper subject for your email and include your name and student ID. It is strongly encouraged to use the @Sharif webmail service to ensure easy identification by the instructional team.)
- arashmarioriyad@gmail.com
- s.emad.emamjomeh@gmail.com
- ilyamahrooghimath@gmail.com
- amirmmdizady@gmail.com
Note that you can always directly contact the instructor via his email address.
Knowledge and experience of a general-purpose programming language (Python), Data Structures and Algorithms, and ideally Probability and Statistics (Random Variables, (Joint) Probability Mass Functions, Conditional Probability, Independence, Bayes Theorem) and Linear Algebra. It is highly recommended to have the Engineering Probability and Linear Algebra courses passed before taking this course.
Dr. Rohban's class is scheduled for Sundays and Tuesdays from 15:00 to 16:30 and Mr. Fereydooni's class is scheduled for Sundays and Tuesdays from 16:30 to 18:00. By default, classes will be held in person; however, under special circumstances, the class might be held virtually with prior announcements. The virtual class link of Dr. Rohban's group is https://vc.sharif.edu/ch/rohban.
Please note that grades will be calculated out of $22.0$, with the respective grades for each section provided below:
- Homework Assignments: $7.5 + 1$ points.
- Five Series, each $1.5$ points and $1$ bonus point in total.
- Presentation: $1 + 1$ point.
- Quiz: $1.5$ points.
- Midterm Exam: $4.5$ points.
- Final Exam: $5.5$ points.
Other information about the grading policy is provided in the Policy Document.
Please note that grades will be calculated out of $22.0$, with the respective grades for each section provided below:
- Homework Assignments: $6$ points.
- Five Series
- Quiz: $+2$ points.
- Midterm Exam: $6$ points.
- Final Exam: $8$ points.
More information about the grading policy is provided in the Policy Document.
Students' learning will be assessed through two quizzes, a midterm, and a final exam. These exams will be held on the following dates, and participation is mandatory for all students.
- First Quiz: 1403/08/20 12:00 PM.
- Second Quiz: 1403/10/09 12:00 PM.
- Midterm Exam: 1403/09/22 15:00 PM.
- Final Exam: 1403/11/04 15:00 AM (as scheduled in EDU).
More information about the exams is available in the Schedule Sheet.
Students' learning will be assessed through ten quizzes, a midterm, and a final exam. The midterm and final exams will be held on the following dates, and participation is mandatory for all students.
- Midterm Exam: 1403/09/22 15:00 PM.
- Final Exam: 1403/10/30 15:00 AM (as scheduled in EDU).
More information about the exams is available in the Schedule Sheet.
Five series of homework assignments will be distributed in this course. Assignments will be released on Saturday midnight every other week. The answers must be submitted on Dr. Rohban's Quera and Mr. Fereydooni's Quera. Regarding the late submission policy, you can submit answers with total delay of 10 days with no penalty through the semester. Any additional delays will lead to a 0.5% reduction in the assignment grade for every hour of delay. This is illustrated in the figure below, where ℎ representsthe amount of delay in hours:
There will be a zero-tolerance policy for cheating or copying homework assignments. The first time you are caught, we will refer you to the committee, and you will fail the course. Please refer to the Education Committee's statement on homework etiquette.
- https://sut-ai.github.io/: This is the main page of our course and all exercises, slides and course resources will be placed in it.
- Dr. Rohban's Quera and Mr. Fereydooni's Quera: Delivery of exercises and announcements will be done entirely through Quera. Make sure you enter an email on Quera that you check regularly. Your questions from the exercises will also be answered in Quera.
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1 |
Date:
Sun, Mehr 1 |
Description:
Introduction & Intelligent Agents |
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2 |
Date:
Tue, Mehr 3 |
Description:
Uninformed Search |
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3 |
Date:
Sun, Mehr 8 |
Description:
Uninformed Search |
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4 |
Date:
Tue, Mehr 10 |
Description:
Informed Search |
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5 |
Date:
Sun, Mehr 15 |
Description:
Informed Search |
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6 |
Date:
Tue, Mehr 17 |
Description:
Local Search |
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7 |
Date:
Sun, Mehr 22 |
Description:
Search in Continuous Spaces |
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8 |
Date:
Tue, Mehr 24 |
Description:
Constraint Satisfaction Problems |
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9 |
Date:
Tue, Mehr 29 |
Description:
Constraint Satisfaction Problems |
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10 |
Date:
Tue, Aban 1 |
Description:
Adversarial Search |
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11 |
Date:
Sun, Aban 6 |
Description:
Uncertainty & Inference |
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12 |
Date:
Tue, Aban 8 |
Description:
Bayesian Networks: Representation |
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13 |
Date:
Sun, Aban 13 |
Description:
Inference in Bayesian Networks: Exact |
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14 |
Date:
Tue, Aban 15 |
Description:
Inference in Bayesian Networks: Approximate |
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15 |
Date:
Sun, Aban 20 |
Description:
Temporal Probability Models: Markov Chains & HMMs |
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16 |
Date:
Tue, Aban 22 |
Description:
Temporal Probability Models: Particle Filters |
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17 |
Date:
Sun, Aban 27 |
Description:
Learning in Bayesian Networks & Naive Bayes |
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18 |
Date:
Tue, Aban 29 |
Description:
Decision Tree |
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19 |
Date:
Sun, Azar 4 |
Description:
Concepts of Machine Learning |
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20 |
Date:
Tue, Azar 6 |
Description:
Regression & Optimization |
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21 |
Date:
Tue, Azar 11 |
Description:
Perceptron |
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22 |
Date:
Sun, Azar 13 |
Description:
Neural Networks |
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23 |
Date:
Sun, Azar 18 |
Description:
Neural Networks |
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24 |
Date:
Tue, Azar 20 |
Description:
Markov Decison Process |
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25 |
Date:
Sun, Azar 25 |
Description:
Value Iteration & Policy Iteration |
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26 |
Date:
Tue, Azar 27 |
Description:
Reinforcement Learning: Passive |
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27 |
Date:
Sun, Dey 2 |
Description:
Reinforcement Learning: Active |
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28 |
Date:
Sun, Dey 4 |
Description:
Reinforcement Learning: Approximate |
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29 |
Date:
Sun, Dey 9 |
Description:
Applications / Guest Lecturer |
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30 |
Date:
Sun, Dey 11 |
Description:
Applications / Guest Lecturer |
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