About
Undergrad & Software Engineer
Hi, I'm a current undergraduate at McGill, studying Computer Science with a minor in Mathematics.
- City: Montreal, Canada
- Phone: +1 438 866 3807
- University: McGill University
- E-mail: sinan.gulan@mail.mcgill.ca
Courses
Applied Machine Learning
COMP 551
This class is the first graduate-level course I had at McGill. It is an intensive introduction to supervised machine learning models as well as an overview of the history and context of AI.
Key takeaway: Since machines can seamlessly consume and manipulate incredible amounts of data, they can outperform humans on complex tasks, given the task is well-defined. However, the dream of a generalized AI seems far away.
Operating Systems
COMP 310
This class served sort of as a de-mystification of smart machines that are omnipresent in our daily lives. It was interesting exploring the frontier between software & hardware.
Key takeaway: Amdahl's law, which states that adding more processors to complete a job has diminishing returns. I've seen that this principle could be applied to almost any other domain in life.
Distributed Systems
COMP 512
This class was a implementation-based, somewhat low-level (although very theoretical at times) course, which I enjoyed quite a bit. I got to learn about and implement the Paxos consensus algorithm, which was quite challenging but also rewarding.
Key takeaway: Theory only gets you so far: great products require a lot of engineering.
Compiler Design
COMP 520
This course brought many of the seemingly independent concepts studied throughout my time in University together. I got to learn a new language (Scala), to write a Compiler for a subset of C and developed a solid understanding of what's happening under the hood.
Key takeaway: Testing is key.
Applied Machine Learning
COMP 551
This class is the first graduate-level course I had at McGill. It is an intensive introduction to supervised machine learning models as well as an overview of the history and context of AI.
Key takeaway: Since machines can seamlessly consume and manipulate incredible amounts of data, they can outperform humans on complex tasks, given the task is well-defined. However, the dream of a generalized AI seems far away.
Operating Systems
COMP 310
This class served sort of as a de-mystification of smart machines that are omnipresent in our daily lives. It was interesting exploring the frontier between software & hardware.
Key takeaway: Amdahl's law, which states that adding more processors to complete a job has diminishing returns. I've seen that this principle could be applied to almost any other domain in life.
Distributed Systems
COMP 512
This class was a implementation-based, somewhat low-level (although very theoretical at times) course, which I enjoyed quite a bit. I got to learn about and implement the Paxos consensus algorithm, which was quite challenging but also rewarding.
Key takeaway: Theory only gets you so far: great products require a lot of engineering.
Compiler Design
COMP 520
This course brought many of the seemingly independent concepts studied throughout my time in University together. I got to learn a new language (Scala), to write a Compiler for a subset of C and developed a solid understanding of what's happening under the hood.
Key takeaway: Testing is key.
Applied Machine Learning
COMP 551
This class is the first graduate-level course I had at McGill. It is an intensive introduction to supervised machine learning models as well as an overview of the history and context of AI.
Key takeaway: Since machines can seamlessly consume and manipulate incredible amounts of data, they can outperform humans on complex tasks, given the task is well-defined. However, the dream of a generalized AI seems far away.
Operating Systems
COMP 310
This class served sort of as a de-mystification of smart machines that are omnipresent in our daily lives. It was interesting exploring the frontier between software & hardware.
Key takeaway: Amdahl's law, which states that adding more processors to complete a job has diminishing returns. I've seen that this principle could be applied to almost any other domain in life.
Distributed Systems
COMP 512
This class was a implementation-based, somewhat low-level (although very theoretical at times) course, which I enjoyed quite a bit. I got to learn about and implement the Paxos consensus algorithm, which was quite challenging but also rewarding.
Key takeaway: Theory only gets you so far: great products require a lot of engineering.
Compiler Design
COMP 520
This course brought many of the seemingly independent concepts studied throughout my time in University together. I got to learn a new language (Scala), to write a Compiler for a subset of C and developed a solid understanding of what's happening under the hood.
Key takeaway: Testing is key.