BIL 682  Artificial Intelligence (Spring 2013)
Lectures: Tuesday 13:3016:15@D7
A comic strip by Tom Gauld
Instructor: Aykut Erdem
email: aykutatcs.hacettepe.edu.tr
Office: 111
Tel: 297 7500 / 146
Office hours: By appointment.
Course Description:
This is a graduatelevel introductory course in Artificial Intelligence (AI) which will give a broad overview of many concepts and algorithms in AI, starting from basic topics like problem solving by searching, game playing, supervised and unsupervised learning to more recent topics such as online learning, ranking and structural learning. The goal is to provide students with a deep understanding of the subject matter and skills to apply these concepts to real world problems.
Prerequisites:
Basic algorithms, data structures. Basic probability and statistics. Basic linear algebra and calculus. Good programming skills.
Reading Assignments:
Each week a number of research papers will be assigned as reading assignments. Students are expected to write a brief review of any of the assigned papers (less than a page) for half of the papers. Each review should summarize the paper in 45 sentences. The review should clearly identify the main contribution of the paper and describe the strengths and weaknesses of the paper. The reviews should be emailed to the instructor before class (by 13:00 on Tuesdays). Each student has 3 late days for the entire semester, but beyond that limit his/her submission will not be accepted.
Programming Assignments:
There will be three assignments related to the topics covered in the class. Each assignment will involve implementing an algorithm, carrying out a set of experiments to evaluate it, and writing up a report on the experimental results. All assignments have to be done individually, unless stated otherwise.
Project:
The students taking the course will be required to do a project which should be done in a team of at most two students. For a detailed description of the course project, follow this link. In preparing your progress and final project reports, you should use the provided template and submit them electronically in PDF format.
Grading Policy:
Class participation 
5% 
Reading Assignments 
12% 
Programming Assignments 
18% 
Project 
30% 
Final Exam 
35% 
Course Schedule:
Week 
Date 
Topic 
Assignments 
Additional Readings 
1 
2/26 
Introduction to Artificial Intelligence [slides] 


2 
3/5 
Problem Solving and Search [slides] 

3 
3/12 
Game Playing [slides] 


4 
3/19 
Introduction to Machine Learning, Naïve Bayes Classifier
Logistics Regression [slides]



5 
3/26 
Instance based learning, Support vector machines [slides]

PSet 1 due 

6 
4/2 
Boosting, Model Selection [slides]



7 
4/9 
Clustering, Unsupervised Dimensionality Reduction [slides]



8 
4/16 
SemiSupervised Learning, MultipleInstance Learning [slides] 


9 
4/23 
No class (The Republic Day) 

10 
4/30 
Learning with structured inputs and outputs [slides]

PSet 3 due 

11 
5/7 
Learning to Rank, MultipleKernel Learning, Online Learning [slides]



12 
5/14 
Kernel Density Estimation, Kernel Regression, Outlier Detection [slides]



13 
5/21 
Project Presentations 
 
14 
5/28 
Project Presentations (continued) 
 
Resources:
Communication:
The course webpage will be updated regularly throughout the semester with reading assignments and important deadlines. All other communications will be carried out through Piazza. Please enroll it by following the link https://piazza.com/hacettepe.edu.tr/spring2013/bil682