|
Overview
Algorithms and representations used in artificial intelligence. Introduction and implementation of algorithms for search,
planning, perception, knowledge representation, logic,
probabilistic representation and reasoning, robotics,
and machine learning.
Some portions of the course will be based on material from Ron Parr's
Spring 2014 Intro to AI course.
Instructor
George Konidaris
Office: LSRC D224
Office Hours: Tue 5-6pm, Thur 11am-12pm
Email: gdk at cs dot duke dot edu
TA
Zhenyu Zhou
Office: N006 North
Office Hours: Mon 10-11am, Thur 4-5pm, at LSRC D301
Email: zzy at cs dot duke dot edu
UTAs
Lien Hoang
Office Hours: Tuesdays 6-8pm at The Link (Perkins Library)
Email: lien dot hoang at duke dot edu
Ang Li
Office Hours: Mon 3-4pm, Sundays 7-8pm, at The Link (Perkins Library)
Email: li dot ang at duke dot edu
[Back to top]
Syllabus
- Agents and Agenthood
- Search
- Uninformed
- Informed
- Mini-Max for Game Playing
- Knowledge Representation and Reasoning
- Propositional Logic
- First-Order Logic
- Reasoning and Logical Inference
- Uncertain Knowledge
- Bayes' Rule
- Probabilistic Reasoning
- Bayes Nets
- Planning
- Task Planning
- Robot Motion Planning
- Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Philosophy of AI
[Back to top]
Prerequisites
You should be
comfortable independently writing and debugging programs in C, C++, or Java.
You should be able to do short proofs.
You should be comfortable with the core computer science concepts
of computational complexity and the analysis of algorithms.
You should know some calculus.
Basic probability and statistics are helpful but not required.
[Back to top]
Schedule
The first class is on January 9th. The class meets on a
Wednesday-Friday schedule, from 3:05pm to 4:20pm in Soc Psy 126.
Date | Topic | Homework | Slides |
January 9th |
Introduction |
Read chapters 1 and 2
install
python
Download search.zip
Run pacman.py (complete ASAP)
Familiarize yourself with Python
(Nothing to turn in)
Eclipse users, try python
console in eclipse |
PDF |
January 14th |
Uninformed search. |
Read chapter 3 (up to but not including 3.5) |
PDF |
January 16th |
Informed Search |
Read the remainder of Chapter 3. |
PDF
A* proof |
January 21st |
Python Tutorial |
|
PDF |
January 23rd |
Game Playing and Minimax Search |
Read chapter 5, up to and including 5.4.
Homework 1 assigned, due February 6th. |
PDF |
January 28th |
Game Theory (Ron Parr) |
|
PDF |
January 30th |
Game Theory (Ron Parr) |
|
(continued) |
February 4th |
Knowledge Representation and Reasoning: Propositional Logic |
Chapter 7 |
PDF |
February 6th |
KRR: First-Order Logic |
Chapters 8 and 9. |
PDF |
February 11th |
Uncertainty |
Chapter 13.
Optional: The Cancer Cluster Myth,
and The Hot Hand in Basketball: On the Misperception of Random Sequences.
Homework 2 assigned, due February 24th (midnight). |
PDF |
February 13th |
Bayesian Networks |
Chapters 13 and 14 (up to 14.4). |
PDF |
February 18th |
Cancelled due to weather. |
|
|
February 20th |
Bayesian Networks II |
|
PDF |
February 25th |
Hidden Markov Models |
Chapter 15 (up to and including 15.3) |
PDF |
February 27th |
Planning |
Chapter 10 |
PDF |
March 4th |
Probabilistic Planning |
Chapter 17 (up to and including 17.3) |
PDF |
March 6th |
No class |
|
|
March 11th |
Spring break |
|
|
March 13th |
Spring break |
|
|
March 18th |
Robot Motion Planning |
Sections 25.4 - 25.6 (inclusive) |
PDF |
March 20th |
Midterm |
|
|
March 25th |
Machine Learning Intro |
Chapter 18 |
PDF |
March 27th |
Machine Learning II |
Homework 4 assigned, due April 9th. |
PDF |
April 1st |
Unsupervised learning. |
|
PDF |
April 3rd |
No class |
|
|
April 8th |
Unsupervised & Reinforcement Learning |
Chapter 21. Also Wikipedia entries on
Principal Component
Analysis and ISOMAP. |
PDF |
April 10th |
Reinforcement Learning |
Homework 5 assigned, due April 17th. |
PDF |
April 15th |
Philosophy of AI |
Chapter 26 |
PDF |
April 17th |
No class |
|
|
Final exam: Thursday April 30th, 2pm-5pm.
[Back to top]
Assignments
Assignment | Available | Due |
HW1 | January 23rd | February 6th |
HW2 | February 11th | February 24th (midnight) |
HW3 | March 1st | March 6th (midnight) |
HW4 | March 27th | April 9th (midnight) |
HW5 | April 10th | April 17th (midnight) |
What to turn in:
- For written answers, please turn in a PDF. Do not turn in
Microsoft Word or other formats.
- For programming questions, please include your code and any supporting
files needed, along with some examples of how your code runs. If any
special instructions are needed for compiling or executing your code,
please include this in a readme.txt file. If it is not obvious how to
make your code work, you may not receive full credit!
- It is not necessary to embed an extra copy of your code within your
written solutions. The files you submit are sufficient for this
purpose. However, if you want to reference something in one your
files, please do so in a clear manner by mentioning specific file
names and functions.
- Include your name in all files that you submit. Put it at the
top of the first page of your written solutions and in the comments
to each file you submit for your code.
- Please avoid turning in scanned, handwritten assignments. This
is permitted but discouraged because slows down the grading
process and because it leads to very large files. If there is some emergency that necessitates submitting scanned material, pick a DPI and compression
level that will lead to reasonable file sizes.
- Include figures directly in your PDF rather than
as separate attachments.
How to turn it in:
- Unless otherwise stated, solutions are due immediately before class on the due date.
- Homework assignments should be turned in electronically through
Sakai.
- Include your name as part of the file name of the file you upload
to sakai. (It is confusing for the TAs if they need to wade
through 60 files all named hw.pdf.) If your assignment involves
multiple files, please put them in a single folder/directory and zip
them. Do not use other compression methods such as zoo, rar, etc.
Academic Honesty
- We take academic honesty very seriously. This matrix should leave no ambiguity about what is permitted and what is not permitted.
You should check if you have any confusion about what is permitted.
Lateness policy
- You may request an extension before the due date of the
assignment. Valid reasons for extensions include (but are not
necessarily limited to) interviews, travel
for research or academic purposes, and illness.
- Late assignments (without a previously granted extension) will be
penalized 10% per day. Assignments will not be accepted more than 5
days after the due date.
[Back to top]
Grading
Graded components will be the homeworks (40%), the midterm (30%),
and the final exam (30%).
I expect all Duke students to
conduct themselves with the highest integrity, according to the
Duke Community Standard. If you are unsure what this means,
please refer to this link.
For a more concrete description, this matrix outlines what
forms of collaboration with others are and are not allowed during this
course.
[Back to top]
Resources
Required Text
Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig.
Be sure to check for errata! (scroll down for a list of current errata)
[Back to top]
|