Introduction Comprehensive AI Course iAAA
Your first step into the world of AI engineering
The iAAA AI course is not just a training course; it is a launchpad for everyone interested in AI who wants to learn this technology in a comprehensive and practical way.
Designed in collaboration with instructors and specialists in the field, this course is tailored for students, beginners, and even professionals to learn practical, project-based AI skills in a simple, structured format.
Most importantly, this course is a gateway to one of the country’s largest AI competitions — the Iran Annual AI Award (iAAA).
Why join this course?
- Project-based AI learning from basics to advanced
- Learn topics from multiple instructors
- Work with real data and solve practical problems
- Learn to build, present, and document models
- Practice and Q&A sessions
- Active online support
- Personalized learning content
- Supplementary workshops and webinars
- Developing soft skills and teamwork
- Preparation to enter the iAAA competition
- Award of a bilingual iAAA recognized certificate
- Option to earn university internship credit
- Internship and hiring opportunities
- Membership in Iran’s active AI community
Course Information
Start date:
11 August 2025
Number of weeks:
12
Duration:
240 hours
Number of instructors:
16
Syllabus
Foundations
Topic
- Introduction to AI and Competitions
Description
- Install Python, Jupyter Notebook, Overview of AI tools and platforms
Python for AI
Topic
- Introduction to Programming
- Basic Syntaxes
- Conditional Blocks
- Loops
- Data Structures & Strings
- Functions
- Files
- Object-Oriented Programming
- Lambda Functions
- Use of Frameworks and Packages
- Practical Project
Description
- Basics of Python, IDEs, and basic programming exercises
- Variables, operators, and basic Python syntax
- if-elif-else conditions and logical operators
- For and While loops, control statements
- Lists, tuples, dictionaries, and string operations
- Defining functions, arguments, and recursion
- File handling, reading, writing, and appending
- Classes, objects, inheritance, and encapsulation
- Anonymous functions, map, filter, and reduce
- Installing and using NumPy, Pandas, and Matplotlib
- Build an attendance tracker application
Mathematics for Machine Learning
Topic
- Linear Algebra
- Probability and Statistics
- Calculus
- Optimization Techniques
Description
- Vectors, matrices, PCA, and practical exercises with NumPy
- Distributions, Bayes’ theorem, and practical exercises with NumPy
- Derivatives, gradients, and gradient descent
- Convex optimization and advanced techniques
Data Science for AI
Topic
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Data Visualization
Description
- Handle missing data, scaling, normalization, encoding categorical variables
- Visualize data distributions and compute descriptive statistics
- Feature selection, creation, and scaling
- Advanced visualizations using Matplotlib, Seaborn, and Plotly
Digital Image Processing
Topic
- Introduction to Images
- Basic Image Operations
- Histograms in Image Processing
- Convolution and Fourier Transform
- Noise Reduction
- Edge Detection
- Binary Image Processing
- Segmentation
- Image Registration
Description
- Basic concepts, formats, and Python setup
- Cropping, resizing, color spaces, and practical exercises
- Understanding and plotting histograms
- Filters and frequency domain transformations
- Noise types and denoising techniques
- Sobel and Canny edge detection
- Thresholding, morphology, and contours
- Watershed algorithm for image segmentation
- Align images using keypoints and transformations
Machine Learning
Topic
- Introduction to Machine Learning
- Supervised Learning (Regression)
- Supervised Learning (Classification)
- Unsupervised Learning (Clustering)
- Unsupervised Learning (Dimensionality Reduction)
- Model Evaluation and Tuning
- Ensemble Learning
- Real-World Applications and Project
Description
- Overview of ML types and pipelines
- Linear and polynomial regression with Scikit-learn
- Logistic regression, KNN, decision trees
- K-Means and hierarchical clustering
- PCA and feature selection
- Cross-validation and hyperparameter optimization
- Random Forest, Gradient Boosting, and XGBoost
- End-to-end ML pipeline for a dataset
Deep Learning
Topic
- Introduction to Deep Learning
- Fundamentals of Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
- Transformers and Attention Mechanisms
- Generative Models (GANs and VAEs)
- Model Evaluation and Tuning
- Real-World Project
Description
- Concepts, applications, and PyTorch setup
- Building and training basic neural networks
- Implement CNNs and fine-tune pretrained models
- Sequence modeling and time series forecasting
- Transformers and fine-tuning BERT
- Implementing GANs and VAEs
- Evaluation metrics and regularization
- Build an end-to-end Deep Learning project in PyTorch
End
Instructors
Mohammad Akbari
Speaker
- AI researcher
- Visiting professor, University of London
- Researcher, New York University
- Visiting professor, Iran University of Science and Technology
- PhD, Amirkabir University of Technology
Rashad Hosseini
Deep Learning
- Faculty member, School of Electrical and Computer Engineering, University of Tehran
- Former head of the AI Headquarters, Vice-Presidency for Science and Technology
- PhD in AI, Technical University of Berlin
- Chairman of the Board, Hoosh Afzar Rahbar Ariaman
Ali Zarezadeh
Speaker
- Machine Learning Team Lead, Yektanet
- Team Lead, Mofid Brokerage
Postdoc, a German university - AI researcher and practitioner
- Machine Learning instructor
Sepehr Akbarzadeh
Deep Learning / Computer Vision
- Senior AI Engineer, Irancell
- Software Engineer, Iranian Hospital Qatar
- Elite AI student
- Instructor, Sharif University of Technology
Zeinab Barzegar
Speaker
- Head of the Scientific Committee of the third iAAA
- Faculty member, Tehran University of Medical Sciences
- PhD in AI, Sharif University of Technology
- AI advisor, Hamrah Fund (MCI)
Amin Babadi
Speaker
- AI and game-animation programmer
- AI specialist at Bugbear, Finland
Mohammadreza Beikzadeh
Speaker
- AI instructor in the UK
- Senior advisor at AIENAI, London
Hadi Asheri
Foundations of Machine Learning Mathematics
- CEO of Hoosh Afzar Rahbar Ariaman
- Member of the AI Commission of the ICT Guild
- AI instructor and practitioner
Masoud Mazloum
Speaker
- CEO of the Data Science Innovation Center
- PhD and Postdoc, University of Amsterdam, Netherlands
- Visiting professor, University of Amsterdam
- Data science instructor and researcher
Aliakbar Kiaei
Machine Learning
- Faculty member, Malek-Ashtar University of Technology
- Postdoc in AI
- AI instructor and practitioner
Soheil Tehranipour
Computer Vision
- CEO of Saeeyan Ertebat
- Head of the AI team at Fakher Holding
- Former instructor, Sharif University of Technology
- AI researcher
Masoud Kaviani
Data Science
- Machine Learning Engineer, Zebracat, Berlin
- Senior Data Scientist, Saba Idea (Aparat, Filimo, SabaVision)
- Visiting professor, Computer Department, Shahid Rajaee University
- Visiting instructor, ACECR, Sharif University of Technology
- Faculty member, Data Governance Group, Data Science Innovation Center
- Recipient of an entrepreneurship medal from Google
- Top rank at Iran Startup Weekend
Elnaz Amanzadeh
Speaker
- Head of the AI department, Brain Research Center
- AI researcher in medical sciences, Shahid Beheshti University
- AI instructor and practitioner, University of Medical Sciences
Fatemeh Razavi
Python Basics
- PhD in AI, University of Tehran
- AI instructor and practitioner
Mohammad Fotouhi
Python
- LLM Engineer, Sharif Research Institute
- AI researcher and instructor
Mobin Nesari
Mentor
- MSc in Computer Science and AI
- Scientific advisor and instructor of the second iAAA
- Instructor, Shahid Beheshti University
Siamak Farshidi
Speaker
- Visiting professor, University of Amsterdam, Netherlands
- Visiting professor, Utrecht University, Netherlands
Mohammadmehdi Begmaz
Mentor
- MSc in Computer Science and AI.
- AI instructor, Shahid Beheshti University
Frequently Asked Questions
Is the iAAA course enough to prepare for the competition?
Yes. Even those with no prior background in AI can, by completing the comprehensive zero-to-hero iAAA course, reach a level of skill that enables them to solve the competition's challenges.
Does the course and competition require in-person attendance?
No. All training and competition processes are held entirely online, with no need for physical attendance.
Is group registration possible?
Yes. Group registration is possible, and special benefits are offered for registered teams.
Are there any prerequisites for the course?
No. The course is designed to require no prior knowledge, and anyone interested can take part.
What topics does the course cover?
The training covers specialized AI topics, soft skills, team building, problem solving, and readiness to enter the job market.
What is the course schedule?
The course begins in early July and continues through late September.
On which days are the classes held?
Classes are held on even days from 6 to 9 p.m., and include two teaching sessions and one practice session per week.
Is a certificate issued after the course?
If a participant has viewed at least 60% of the course content and completed the related exercises, an electronic certificate will be issued for them.
Is a physical copy of the certificate available?
No. Currently the certificate is issued electronically only.
Can the course fee be paid in installments?
Yes. The course tuition can be paid in installments.