I am a professor of Computer Engineering at Koç University in Istanbul and the founding director of the Artificial Intelligence Laboratory. Previously I was at the MIT AI Lab and later co-founded Inquira, Inc. My research is in natural language processing and machine learning. For prospective students here are some research topics, papers, classes, blog posts and past students.
Koç Üniversitesi Bilgisayar Mühendisliği Bölümü'nde öğretim üyesiyim ve Yapay Zeka Laboratuarı'nın kurucu müdürüyüm. Bundan önce MIT Yapay Zeka Laboratuarı'nda çalıştım ve Inquira, Inc. şirketini kurdum. Araştırma konularım doğal dil işleme ve yapay öğrenmedir. İlgilenen öğrenciler için araştırma konuları, makaleler, verdiğim dersler, Türkçe yazılarım, ve mezunlarımız.

February 01, 2021

Programlama pratik yaparak öğrenilir: Kumbara Dergisi Röportaj

İş Bankası, Koç Üniversitesi işbirliğinde ülkemizin bilimsel ve akademik faaliyetlerine katkıda bulunmak amacıyla “Yapay Zekâ Uygulama ve Araştırma Merkezi” kurdu. Bu merkezle yapay zekâ alanında ileri düzeyde çalışmalar gerçekleştirilmesi amaçlanıyor. Kumbara Dergisi olarak, Koç Üniversitesi İş Bankası Yapay Zekâ Uygulama ve Araştırma Merkezi Direktörü Prof. Dr. Deniz Yuret’e yapay zekâ alanı ve Yapay Zekâ Uygulama ve Araştırma Merkezi hakkında sorular sorduk.

Tüm Röportaj

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December 29, 2020

Ulaş Sert, M.S. 2020

Contact info:
M.S Thesis: Training a Bridge Bidding Agent using Minimal Feature Engineering and Deep Reinforcement Learning, Koç University, Department of Computer Engineering. December 2020. (PDF, Presentation, Code).

Thesis Abstract:
The game of contract bridge, or just bridge, is a four-player imperfect information card game where two partnerships of two players compete against each other. It has two main phases: bidding and play. While the computer players have approached human-level performance two decades ago in the playing phase, bidding is still a very challenging problem. This makes bridge one of the last popular games where computers still lag behind the expert human-level performance. During bidding, players only know their own cards while participating in a public auction. Performing well in this phase requires the players to figure out how to communicate with their partners using the limited vocabulary of bids to decide on a joint contract. This communication is restricted by the strict ordering of legal bids and can be negatively interfered by bids made by the opponent partnership. In this thesis, we experiment with several novel architectures with minimal feature engineering and evaluate them by using supervised training over a data set of expert-level human games. After that, we further study different forms of deep reinforcement learning to refine the resulting model by simulated gameplay. Lastly, we propose an oracle evaluation metric that can measure the quality of any bidding sequence with respect to the game-theoretical optimum.

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