Lecture 1: Introduction

# Artificial Intelligence

## Introduction

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# Instructor and Schedule

* Instructor: Dr. *Markus* Eger
 
 * Email: <a href="mailto:meger@cpp.edu">meger@cpp.edu</a>
 
 * Office hours: Tuesday, 1-2.30pm, Wednesday, 1.30-3pm, Thursday, 1.30-2.30pm
 
 * Use my <a href="https://discord.gg/RFGnqQBU2n">office Discord</a> for office hours!
    
 * Class: Tuesday, Thursday, 4-5.15pm
 
 
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# About Me

* Originally from Austria
 
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# About Me

<img src="/CI-2700/assets/img/austria.png" width="70%"/>
 
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* Originally from Austria
 
 * BSc and MSc in Computer Science from University of Technology Graz, Austria 
 
 * PhD in Computer Science from NC State University, USA

* Visiting professor at the University of Costa Rica 2019-2020

* I work on AI for games involving communication and/or cooperation (Hanabi, Werewolf, Pandemic, etc.)
 
 * Games I play: Warframe, Guild Wars 2, Incremental Games 
 
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# About Me

<img src="/CI-0129/assets/img/cats.jpg" width="90%"/>
 
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# About You

* Name
 
 * Games
 
 * Fun facts?
 
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# Class Resources

* Blackboard
  
  * Lecture Notes
  
  * <a href="https://discord.gg/dm8Sy7f9YH">Discord</a>
  
  * **Everything** is linked to on Blackboard 
  
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class: mmedium
  
# Class contents
  
  * What is AI?
  
  * Intelligent Agents 
  
  * Classical Search
  
  * Adversarial Search
  
  * Logic 
  
  * Planning 
  
  * Machine Learning
  
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class: small
  
# Textbook
  
<img src="/CS4200/assets/img/textbook.png" width="40%"/>

AI: A Modern Approach, 3rd (shown) or 4th edition

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class: small
  
# Auxiliary Textbook
  
<img src="/CS4200/assets/img/mltextbook.png" width="40%"/>

<a href="https://www.manning.com/books/deep-learning-with-pytorch">Free Online Version</a>

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# Grading
   
* Labs: 45%
     - Search: 10%
     - MCTS: 10%
     - Logic: 10%
     - Neural Networks: 15%

* Quizzes: 15%
     - One short quiz per week, worth 1.5%
     - Only the best 10 count towards your grade

* Exams: 40%
     - Midterm: 15%
     - Final (cumulative!): 25%

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# Python

* The labs in this class will be in Python

* This is particularly important for the last lab (Neural Network)

* The first labs are also there for you to get used to Python

* <a href="https://github.com/galaxykate/PythonCheatsheet/blob/main/pythonCheatsheet.md">Python Cheatsheet</a> by <a href="https://twitter.com/GalaxyKate">Kate Compton</a>

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class: small
  
# Class schedule
  
  * 1/26 - 1/28: What is Intelligence?
  
  * 2/2 - 2/18: Search and Adversarial Search 
  
  * 2/25 - 3/11: Logic 
  
  * 3/16: Review 
  
  * 3/18: Midterm
  
  * 3/25 - 4/8: Machine Learning (overview)

* 4/13 - 5/4: Neural Networks

* 5/6 - 5/11: AI and Humans
  
  * 5/13: Review
  
  * 5/20: Final

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# Labs

* There will be four lab assignments, each with 3-4 weeks of time

* I will present each assignment during one full lecture

* After each lab, submit your code and a report (pdf!) to Blackboard

* For the lab, you may work in groups of up to two students

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# Labs

* Lab 1, 2/9: Search/pathfinding (deadline: 3/2)
 
 * Lab 2, 2/23: Monte Carlo Tree Search (deadline: 3/23)
 
 * Lab 3, 3/23: Logic Reasoning (deadline: 4/20)
 
 * Lab 4, 4/20: Neural Networks (deadline: 5/11)
 
All deadlines are on Tuesdays, AoE (Anywhere on Earth)
  
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# Some resources

* (Inofficial) [CPP CS Student Discord](https://discord.gg/pYjC82F)

* [Library Events for CS Majors](https://cpp.libcal.com/calendar/events?t=d&q=Computer%20Science&cid=5014&cal=5014&inc=0)

* [Research Experience for Undergraduates](https://www.cpp.edu/bigdatasec-reu/index.shtml)