# Artificial Intelligence

## July 2018 - Harbin (HIT)

This course is/was run for a selected group of Harbin students over July 10-19 2018

## LaoShi (Teachers)

- David Suter - dsuter AT cs.adelaide.eu.au
- Qince Li - qinceli AT hit.edu.cn

## Textbook

Artifical Intelligence: A Modern Approach (we may abbreviate it as AIMA) is reasonably essential reading. You can find details here. The lectures may sometimes stray beyond the contents of this book.
## Schedule

Note: a course delivery is a living thing - it adapts to the environment and circumstance. Hence anything published here is only approximate. The schedule (see lectures below) may evolve.
## Assessment

Two assignments ("pen and paper") and one programming assignment (with report).
Email your submission to Qince Li （qinceli@hit.edu.cn）. NOTE A hard copy must also be submitted to Qince Li ( at Xinjishu Lou 901)
## Assignments

- Assignment 1 Distributed Thursday July 12. Due Thursday July 19
- Assignment 2 Distributed Tuesday July 17. Due Tuesday July 24
- The Programming Assignment/Report is based on Berkeley Pacman Prac Distributed Thursday July 12. Due Tuesday July 24. The report must contain the following: 1. Introduction-Understanding about the project; 2. Method- the algorithm; 3 Algorithm implementation and results. And the code and the comments should be included in the Appendix. You will also have access to the python autograder to give you feedback as you develop - quote those results in your code. Your code must be in a zip file.
Email your submission to Qince Li （qinceli@hit.edu.cn）. NOTE A hard copy of the report must be submitted to Qince Li ( at Xinjishu Lou 901)

## Lectures

The lecture slides will be posted here - sometimes in advance, sometimes in arrears.
- Tuesday 10th
- (approx 8am-9.30am) Introduction PDF
- (approx. 9.45am-11.30am)Uninformed Search PDF

- Thursday 12th
- (approx. 8am-8.50am) Informed Search PDF
- (approx. 8.55am-9.45am) Adversarial Search PDF
- (approx 10am-10.50am) Probability Primer PDF
- (approx 10.55-11.45am) Bayes Nets PDF
- Java applet for Bayes Net Simulation Here (Try web based applet or download jar file and run from commandline with java -jar bayes.jar)

- Tuesday 17th
- (approx. 8am-8.50am) Bayes Nets Independence PDF
- (approx. 8.55am-9.45am) Bayes Net Inference PDF
- (approx 10am-10.50am) Bayes Net Approx. Inference PDF
- (approx. 10.55am-11.45am) Naive Bayes PDF

- Thursday 19th
- (approx. 8am-8.50am) Decision Trees PDF
- (approx. 8.55am-9.45am) Forms of Learning and NN methods PDF
- (approx. 10am-10.50am) Clustering and Unsupervised Learning perhaps touch on Semi-supervised LearningPDF
- (approx. 10.55am-11.15am) Single Layer Neural Networks PDF
- (approx. 11.15am-11.45am) Multi-Layer Perceptrons PDF

## Homework - work outside of class

Each institution has policies relating to how much work is expected of a students, in total. This expectation includes that students will follow up lectures by reading the associated texts, by reviewing the notes etc. It also includes the expectation that in addition to work that is directly related to lectures, assginments, and labs in terms of formal contact, that students devote a considerable amount of time to completing assignments.
We have scheduled approx. 16hrs of lectures.
It would not be unreasonable to expect that students spend at least two times that quantity in outside work. Of course the amount of time a student must devote depends on their efficiency, their preparation, and on their abilities.
Something close to 50-60hrs in total. Yes it will be a busy two weeks!

### First Task!

Your first assignment will be done in Python. Thus, after the first lecture on Tuesday (no scheduled lecture on Wednesday) you have a couple of days to take a crash course in python. See here