Introduction to Artificial Intelligence
3/30/2020 Homework 4 deadline has been extended to 11:59 pm on 3/31.
2/25/2020 Midterm exam 1 solution has been posted.
1/22/2020 We will use Python 3.7 to run and grade the programming assignments.
Two midterm exams are scheduled as follows:
Midterm Exam 1: Tuesday, 2/25/2020 (in class)
Midterm Exam 2: Thursday, 4/9/2020 (in class)
Access to the following pages requires a Purdue login.
Super brief history of deductive AI
Recent AI history, Intro to dynamic programming
Dynamic programming, introduction to regression
Stochastic gradient descent, regularization
Classification, logistic regression, hinge loss
Cross-entropy loss (log loss), K-means unsupervised learning
Named entity classification
K-nearest neighbor demo
State-space search, dynamic programming
Uniform cost search
A* search: admissibility, consistency; problem relaxation
Iterative deepening, best-first search, beam search, Boolean satisfiability
Local search and optimization
Basic neural networks
Group-allowed project; multi-layer neural net, intro to deep learning
Online course plans; more intro to deep nets
Online lecture 1: Convolutional neural networks
Online lecture 2:
(2a) Recurrent neural networks (RNNs) and Long Short-term Memory networks (LSTMs);
(2b) Intro to Markov Decision Processes (MDPs)
Online lecture 3: Markov decision processes policy evaluation
Sample LaTeX template. You may find this tool useful: Detexify.
You can edit and compile LaTeX online on Overleaf. Alternatively you can install LaTeX compiler to your computer so that you can compile offline too.
(Solution links will not work until 24 hrs after due date)
Maintained by Bob Givan and course staff