Teaching
Passionate about educating the next generation of computer scientists and fostering critical thinking in artificial intelligence and machine learning
Teaching Philosophy
My approach to education in computer science and artificial intelligence
"Education is not the filling of a pail, but the lighting of a fire." This quote by W.B. Yeats perfectly captures my teaching philosophy. I believe that effective education in computer science goes beyond transmitting knowledge—it's about inspiring curiosity, fostering critical thinking, and empowering students to become lifelong learners.
In my courses, I emphasize hands-on learning experiences that bridge the gap between theoretical concepts and practical applications. Students work on real-world projects, collaborate on research initiatives, and engage with cutting-edge technologies. I believe that learning by doing not only reinforces understanding but also builds confidence and problem-solving skills essential for success in the rapidly evolving field of computer science.
I am particularly committed to creating inclusive learning environments where students from diverse backgrounds feel valued and supported. By incorporating ethical considerations into technical discussions and highlighting contributions from underrepresented groups in computing, I aim to prepare students who are not just technically proficient but also socially conscious leaders in technology.
Interactive Learning
Engaging students through collaborative projects, discussions, and peer learning
Practical Application
Connecting theoretical concepts to real-world problems and industry practices
Inclusive Environment
Fostering diversity and creating supportive spaces for all learners
Current Courses
Courses I'm teaching this academic year
Cloud Computing
Comprehensive study of cloud computing concepts, architectures, virtualization, and distributed computing systems.
Key Topics:
Machine Learning
Fundamentals of machine learning including supervised and unsupervised learning, neural networks, and practical applications.
Key Topics:
Deep Learning
Advanced deep learning techniques including CNNs, RNNs, GANs, and modern architectures for various applications.
Key Topics:
Course History
Previously taught courses with student feedback
Computer Networks
Semesters Taught:
Algorithms
Semesters Taught:
Discrete Mathematics
Semesters Taught:
Introduction to Machine Learning
Semesters Taught:
Research Methods in Computer Science
Semesters Taught:
Teaching Recognition
Awards and recognition for teaching excellence
Office Hours & Student Support
Available times for academic guidance, research discussions, and career advice
Office Hours
Tuesday
2:00 PM - 4:00 PM
Room 322, IST Building
Thursday
10:00 AM - 12:00 PM
Room 322, IST Building
By Appointment
Email to schedule
Online or In-person
Note: For appointments outside office hours, please email me at least 24 hours in advance. I'm also available for brief questions before/after class.
Contact Information
Office Phone
+1 (555) 123-4567
Office Location
Room 234, Computer Science Building
University of Technology
What to Bring:
- • Specific questions or problems you're working on
- • Course materials or assignments for reference
- • Your laptop if discussing coding problems
- • Resume if seeking career advice
Student Resources
Course Materials
Lecture notes, assignments, and supplementary resources
Study Groups
Collaborative learning opportunities with peer support
Code Examples
Sample implementations and project templates
Career Guidance
Advice on internships, graduate school, and industry careers
Questions About My Courses?
Whether you're a current student, prospective student, or fellow educator, I'm always happy to discuss teaching and learning opportunities.