Teaching Experience

🎓 Head Teaching Assistant

Introduction to Machine Learning

• Instructor: Prof. Emad Fatemizadeh

Course Overview: This course explores the fundamentals of machine learning and its diverse applications. It integrates both theoretical concepts and hands-on practical skills.

• Theoretical Topics:

  • Probability and Statistics
  • Optimization Techniques
  • Linear Algebra Essentials

• Practical Components:

  • Implementing machine learning algorithms in Python
  • Utilizing libraries such as NumPy, Pandas, and Scikit-learn

Responsibilities:

• Designing and grading assignments
• Conducting office hours and tutorials
• Managing and coordinating teaching assistants

👩‍🏫 Teaching Assistant

Introduction to Machine Learning

• Instructor: Prof. Zarchi

Course Description: A collaborative course for B.Sc. and M.Sc. students in the CE department, covering both foundational and advanced machine learning topics including:

• Language Models
• Computer Vision
• Contrastive Learning

Responsibilities:

• Preparing and grading final exams

Course Highlights:

• Format: Hybrid (In-person for Sharif students, Online for others)
• Enrollment: 40,000 students

Professor Zarchi made his course available online for all students. Over 40,000 students from around the world registered for this class (link to news), and all the course videos are accessible to the public on the course website. It was an honor for me to participate in this charitable effort for the public.

Deep Learning

• Instructor: Prof. Emad Fatemizadeh

Course Overview: An in-depth study of deep learning principles and their real-world applications, blending theoretical insights with practical implementation.

• Practical Assignments:

  • Implementing multi-layer perceptrons with various activation and loss functions
  • Analyzing performance across different datasets

• Theoretical Focus:

  • Optimization algorithms
  • Mathematical foundations of deep learning

Responsibilities:

• Preparing and grading second assignments

Convex Optimization

• Instructor: Prof. Rouhollah Amiri

Course Description: A theoretical course focusing on the principles of convex optimization and dual optimization.

• Key Topics:
  • Convex Sets
  • Convex Functions
  • Convex Optimization Problems

Responsibilities:

• Holding office hours
• Conducting tutorials

Introduction to Machine Learning

• Instructor: Prof. Yassaee

Course Overview: An elective tailored for students in biomedical, digital, communication, and control fields, introducing the essentials of machine learning.

Responsibilities:

• Preparing and grading assignments
• Holding office hours