Teaching in 2016

Scientific English (3rd year undergrad course)

Lecture 1: Introduction to the course, What is an abstract (slides)

Assignment due Monday 25 April in paper form:

  • 1. Choose the abstract of any paper in computer science and check whether they satisfy the assumptions of table 1 given in the lecture. Provide a detailed answer. You must submit a copy of the first page of the article you have chosen.

  • 2. Choose any computer scientist of your liking and describe, as if writing an abstract, his/her biggest contribution to the field.

Example of Answer to question (2) with A. Turing

Although references to thinking machines and artificial beings have appeared in history as early as in ancient Greece (Talos of Crete, bronze robot of Hephaestus), no rigorous definition for intelligence machines was known before that proposed by Alan Turing (1912-1954), known as the Turing test. That test has been proposed to test the ability of a machine to exhibit intelligent behaviour equivalent to that of a human being. To pass that test, a machine must be capable of engaging in a conversation with a human judge through a text-only channel in such a way that the human judge cannot reliably tell whether his interlocutor is a machine or not. Ever since it was proposed, this test has been both influential and criticized, making it one of the most fundamental concepts in the history of AI.

Lecture 2: Writing Introductions (slides)

Lecture 3: Writing Methodology (slides)

Assignment due Monday 2nd in paper form:

  • 2. Consider the 5 introductions contained in this document. Divide (using pen colors) these introductions into paragraphs that mention either :

    • Background information / importance of the research field (1)

    • Previous and/or current research and contributions (2)

    • Gap in our knowledge, problem that needs to be solved, research opportunity (3)

    • Paper contribution and structure (4)

Assignment due Monday 16th in paper form:

Write a summary that explains the scientific content of the video below using between 250 and 300 words.

Statistical Machine Learning, Kyoto U. (Spring 2016)

Part I, Statistical Learning Theory

October 6 - December 1

Recommended reading for derivatives and gradients: Appendix A.4 of this book


Homework 1, due May 9th, send to this this email.