You are currently browsing the tag archive for the ‘Teaching’ tag.

Today I am thinking about the graduate-level AI course I will be teaching next semester. This post is mainly for my own reference. I just want to store all the good course material I find in one place. The textbook I will use is the ever-famous AIMA (Artificial Intelligence: A Modern Approach) by Russell & Norvig. It is, as far as I know, the most widely-used AI textbook, and it is well-written.

I hesitated about posting the course websites here, lest students find them and somehow “cheat”. However, after thinking just for a few seconds, I decided not to worry. If they are diligent and interested enough to actually look at the course material in advance, all the better! I don’t have to teach them because they can learn from these other courses on the web! I just have to be careful not to assign homework or exam problems that have solutions on these webpages.

Many thanks to the instructors of these courses. I selected mainly US university courses published in the last couple of years.

Brown University, Spring 2008

UC Berkeley, Fall 2007

UC Berkeley, Fall 2008

CMU, Spring 2007

Georgia Tech, Fall 2007

Harvard, Fall 2008

UIUC, Spring 2007

Advertisements

Here is someone who’s been inspiring me quite a bit lately. In fact, I would like to be influenced by his book and lecture much much more, carrying out more of his advice in my every day life. The video (below) of his “Last Lecture” is great, but I highly recommend the book, The Last Lecture.

RIP.

For the last three days, I participated in the admissions interviews for KAIST. During the whole process, the question that’s been bugging me was, “what kind of a student body do we want?” By “a student body”, I mean the total distribution and population of undergraduate students in terms of their intellectual capacity, curiosity, diligence, academic rigor, talent, passion, and personality.

The format of the interviews–group discussion, individual q&a, 5-minute speech–all seem to favor those with good public speaking skills. But these students, coming from an educational system based on reading, writing, and working on math and science problems on paper, are not accustomed to public speaking, especially in front of professors. The Admissions Office has decided on this interview format because they wanted to recruit students who will become future leaders in science and engineering. I agree wholeheartedly that public speaking skills, along with other leadership skills, are important for future S&E leaders, but still, I could not resist sympathizing with these seventeen-year-olds who were just not trained to speak up. We, the interviewers, were supposed to rate the students on their creativity, thoughtfulness, attitude, and other subjective metrics that were not shown on their school records, but in many cases, I felt the students probably possessed many of these qualities but just could not express them because they were so nervous.

The other professor in my room, who is probably in his late 50s or early 60s, expressed a similar viewpoint several times during the three days. He wanted to give everyone a chance to study at KAIST. I agree with him. After all, these are seventeen (some are sixteen) year-olds who have been working so hard to get into KAIST. I felt that most of the students had plenty of potential to become great leaders, if they are trained well at KAIST. Of course, there is only a limited number of students we can accept each year, but I want to congratulate each and every student who came into our room, “GREAT JOB!”

I both fear and look forward to my first semester of “real” teaching.

I am teaching 1.33 courses next semester. The “1” is for a graduate AI course, and the “.33” is for an advanced undergraduate course that I am co-teaching with two colleagues. I have been thinking about the .33 course lately because we are drafting a course outline for it. The course will be called something like “CS and Probability” or “probabilistic (statistical?) methods in CS”, and as the name suggests, will cover probabilistic (statistical) methods used in solving problems in computer science. We decided to offer this new course because we felt the undergraduates needed a fun introduction to some of the current topics in AI research. (I know that the title does not sound very fun.)

My part of the course will be on statistical NLP. I plan to introduce some interesting problems in natural language processing, present a few empirical approaches, and have the students implement some of the well-known algorithms. There are two nice courses at CMU that I will use as references, though those courses, spanning 2 semesters for master’s level students, cover much much more than I will attempt. Thanks to Roni and Noah for the valuable course material.

Language & Statistics

Language & Statistics II

Advertisements