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 the administrative team only using the course's official email address, sharifaicentral@gmail.com. Avoid sending an email or a message directly to any member of the instruction team. 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. To ask questions about assignments and other course materials, please use Quera. Using Telegram and other messenger services is discouraged. Anything communicated in these messengers by any of the TAs will not be recognized as an official statement and will not be applicable to the grading process. Note that you can always have direct contact with the instructors via their email addresses.
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. Soleymani's class is scheduled for Saturdays and Mondays from 9:00 to 10:30. 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 is
https://vc.sharif.edu/ch/soleymani
Please note that grades will be calculated out of $20$.
- Midterm Exam: $5.0$ points.
- Final Exam: $7.0$ points.
- Homework Assignments: $6.0$ points.
- Quiz: $2.0$ points.
Students' learning will be assessed through five quizzes, a midterm, and a final exam. These exams will be held on the following dates, and participation is mandatory for all students.
- Midterm Exam: 1404/10/20 3:00 PM.
- Final Exam: 1404/11/04 3:00 PM (as scheduled in EDU).
- 1$^{\text{st}}$ Quiz: 1404/08/03 12:15 PM
- 2$^{\text{nd}}$ Quiz: 1404/08/17 12:15 PM
- 3$^{\text{rd}}$ Quiz: 1404/09/08 12:15 PM
- 4$^{\text{th}}$ Quiz: 1404/09/29 12:15 PM
- 5$^{\text{th}}$ Quiz: 1404/10/20 12:15 PM
Five series of homework assignments will be distributed in this course. Students will have 5 days to submit their answers on Quera. Regarding the late submission policy, you can submit answers with total delay of 5 days (maximum of 2 days for each submission) with no penalty. Any more delays and 1% of the assignment grade will be deducted for each hour of delay. Please note that submissions made between midnight (12 AM) and noon (12 PM) will not be considered. You can submit your answers during this interval, but the submission timestamp will be recorded as 12 PM on the same day. Delays will be calculated on an hourly scale, as illustrated in the figure below and described by equation following the figure, where $h$ represents the 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.
- Quera, https://quera.org/course/add_to_course/course/23623/: 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:
Sat Mehr 19 |
Description:
Introduction & Intelligent Agents |
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2 |
Date:
Mon Mehr 21 |
Description:
Uninformed Search & Informed Search |
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3 |
Date:
Sat Mehr 26 |
Description:
Informed Search |
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4 |
Date:
Mon Mehr 28 |
Description:
Local Search |
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5 |
Date:
Sat Aban 3 |
Description:
Search in Continuous spaces |
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Date:
Sun Aban 3 |
Description:
Quiz 1 |
Course Materials:
Sessions 1-4 |
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6 |
Date:
Mon Aban 5 |
Description:
Constraint Satisfaction Problems I |
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7 |
Date:
Sat Aban 10 |
Description:
Constraint Satisfaction Problems II |
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8 |
Date:
Mon Aban 12 |
Description:
Adversarial Search |
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9 |
Date:
Sat Aban 17 |
Description:
Markov Decision Process |
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Date:
Sun Aban 17 |
Description:
Quiz 2 |
Course Materials:
Sessions 5-8 |
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10 |
Date:
Mon Aban 19 |
Description:
Markov Decision Process: Value Iteration & Policy Iteration |
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11 |
Date:
Sat Aban 24 |
Description:
Reinforcement Learning: Passive |
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12 |
Date:
Mon Aban 26 |
Description:
Reinforcement Learning: Active |
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13 |
Date:
Sat Azar 1 |
Description:
Reinforcement Learning: Approximate |
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14 |
Date:
Sat Azar 8 |
Description:
Uncertainty & Inference |
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Date:
Sun Aban 8 |
Description:
Quiz 3 |
Course Materials:
Sessions 9-14 |
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15 |
Date:
Mon Azar 10 |
Description:
Bayesian Networks: Representation |
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16 |
Date:
Sat Azar 15 |
Description:
Inference in Bayesian Networks: Exact |
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17 |
Date:
Mon Azar 17 |
Description:
Inference in Bayesian Networks: Approximate |
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Date:
Thu Azar 20 |
Description:
Midterm Exam |
Course Materials:
Sessions 1-13 |
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18 |
Date:
Sat Azar 22 |
Description:
Temporal Probability Models: Markov Chains & HMMs |
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19 |
Date:
Mon Azar 24 |
Description:
Temporal Probability Models: Particle Filters |
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20 |
Date:
Sat Azar 29 |
Description:
Learning in Bayesian Networks & Naive Bayes |
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Date:
Sun Azar 29 |
Description:
Quiz 4 |
Course Materials:
Sessions 14-19 |
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21 |
Date:
Mon Dey 1 |
Description:
Machine Learning I |
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22 |
Date:
Sat Dey 6 |
Description:
Machine Learning II |
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23 |
Date:
Mon Dey 8 |
Description:
Machine Learning III |
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24 |
Date:
Mon Dey 15 |
Description:
Neural Networks I |
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25 |
Date:
Sat Dey 20 |
Description:
Neural Networks II |
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Date:
Sun Dey 20 |
Description:
Quiz 5 |
Course Materials:
Sessions 20-24 |
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26 |
Date:
Mon Dey 22 |
Description:
Neural Networks III |
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