This is the official webpage for COSC 4600/5600: Fundamentals of Artificial Intelligence for Fall 2017.
Location: 412 Cudahy Hall
Time: TueTh 5:00-6:15 pm
Office hours: TueTh 3:30-5:00 pm
You will find the syllabus here.
- jupyter master website (url)
- anaconda python distribution (url)
- github website (url) and desktop app (url)
- jupyter notebook best practices for data science (url)
- class github repo (url)
- icwsm aaai repo (url)
aug 29: introduction to the course
aug 31: introduction to social networks; read: chapter 1
sep 05: types of social networks; introduction to centrality metrics; read: centrality metrics
sep 07: more centrality metrics
sep 12: introduction to clustering; k-means and dbscan; read: easy book chapter
sep 14: introduction to clustering; spectral and graph based clustering
sep 19: introduction to naive bayes; read: really easy introduction
sep 21: introduction to logistic regression; read: slightly more mathy chapter
sep 26: decision trees and random forests; read: cart + rfs
oct 5: compartmental models – the simple sir; read: chapter 21
oct 10: initial project presentations;
oct 17: introduction to game theory; read: chapter 6
oct 19: no class, fall break
oct 24: guest lecture by Alex Czachor, Senior Data Scientist, Northwestern Mutual
oct 26: no class because of intel deep learning seminar makeup
oct 31: guest lecture by Nithin Ramachandran, Director, Data Science, Direct Supply
nov 02: guest lecture by RJ Nowling, Lecturer and Data Science Engineer, Marquette University
nov 07: nash equilibria, coordination games and multiple equilibria; readings: chapter 6
nov 09: introduction to markets and matching markets; readings: chapter 10
nov 15: modeling matching markets; readings: chapter 10
nov 21: thanksgiving! no class!
nov 23: thanksgiving! no class!
nov 28: final project presentations!
nov 30: introduction to information cascades; readings: chapter 16
dec 05: no class, away to nsf panel;
dec 07: modeling information cascades; readings: chapter 16