CSCI
404 (Spring 2019)
Artificial
Intelligence
[ Administrative Basics  Course Description  Assignments
 Outline of Lectures ]
Lecture 
BB
W250  Tuesday and Thursday 2:00  3:15 PM 
Instructor 
Hua
Wang  BB 280F  Office hours: Tuesday and Thursday 3:15  4:00 PM, or by
appointment 
Grader 
Saad
Elbeleidy  Email: selbeleidy
x mymail y mines y edu
where x=at and y=dot 
Textbook 
Artificial Intelligence:
A Modern Approach, Third Edition. S. Russell and P. Norvig.

Reference
books 
Artificial Intelligence in the 21st Century. Stephen Lucci, Danny Kopec. Artificial
Intelligence: A Systems Approach. M. Tim Jones 
Grading 
Original
percentage of graded components: 1. Homework sets (25%): 5 homework sets, one for 5 points. 2. Projects (30%): 4 programming projects, one for 7.5
points. 3. Midterm (21%): one midterm exam will
be at the middle of the semester. The time will be notified by two weeks in
advance. 4. Final (24%): the final exam will be
at the final exam week specified by school. The time will be notified by school. Your grades have
been/will be posted on the Canvas. If you think the score of your
homework/project/midterm/final is not correct, please appeal it to the
instructor within 10 days from when it is posted in the Canvas.
After 10 days, the score will be considered as finalized. An email will be
sent through the Canvas every time when a score is formally posted. 
Prerequisites 
Good math and programming background. CSCI262 (Data
Structures) and MATH323 (Probability and Statistics for Engineers (I)) are
required. 
This
course gives an introduction to the philosophies and techniques of Artificial
Intelligence. AI techniques have become an essential element in modern computer
software and are thus essential for a successful career and advanced studies in
computer science. Students successfully completing this course will be able to
apply a variety of techniques for the design of efficient algorithms for
complex problems. Topics covered in this course include search algorithms (such
as breadthfirst, depthfirst, A*), gameplaying algorithms (such as Minimax),
knowledge and logic reasoning, probabilistic reasoning, and machine learning.
Topics to Be Covered (Tentative)
1. AI: Concepts and history
2. Solving problems by searching
Uninformed search
Informed search
Constraint satisfaction problems
3. Games
The minimax principle
Modern gameplaying systems
Game theory
4. Logic
Propositional logic
Firstorder logic
Inference
5. Probabilistic reasoning
Basic probability concepts
Bayesian inference
Naive Bayes models
Bayesian networks
6. Machine learning
Supervised vs. unsupervised learning
Decision trees
Nearest neighbor classifiers
Neural networks
Reinforcement
Learning
Five
homework sets have been/will be posted at the Canvas. 

Students will be asked to finish a total of 4
programming projects. The projects must be submitted through the Canvas. See
project details in the Canvas. 
Lecture
notes are posted in the Canvas. You can also access the lecture notes
directly from the below links by your user and password for the Canvas.

Lectures 
Readings 

Week
1 
January
8: Introduction slides January
10: NO CLASS (Day swap by university) 
Chapter
01 
Homework 1
has been posted, due on Tuesday 1/15/2019. 
Week
2 
January
15: Problem solving by search I slides January
17: Problem solving by search II slides 
Section
3.1  3.2 Section
3.1  3.2 
Programming
project 1 has been posted, due on Tuesday 2/5/2019. 
Week
3 
January
22: Uninformed search I slides January
24: Uninformed search II slides 
Section
3.3  3.4 Section
3.3  3.4, 4.3  4.4 
Exercise
1 has been posted. Exercise
2 has been posted. 
Week
4 
January
29: Informed search slides January
31: Admissible heuristics slides 
Section
3.5 Section
3.6 
Exercise
3 has been posted. Homework 2
has been posted, due on Thursday 2/7/2019. 
Week
5 
February
05: Methods for finding optimal configurations slides February
07: Local search slides 
Section
4.1 Section
4.1  4.2 
Exercise
4 has been posted. Exercise
5 has been posted. 
Week
6 
February
12: NO CLASS (Career Day) February
14: Constrained satisfaction problems slides 
Section
6.1  6.2 
Exercise
6 has been posted. 
Week
7 
February
19: NO CLASS (Presidents' Day Break) February
21: Solve constrained satisfaction problems slides 
Section
6.3 
Exercise
7 has been posted. 
Week
8 
February
26: Adversarial search slides February
28: Adversarial search with uncertainty slides 
Section
5.1  5.3 Section
5.4  5.5 
Exercise
8 has been posted. Homework 3
has been posted, due on Tuesday 3/12/2019. Programming
project 2 has been posted, due on Thursday 3/21/2019. Sample
midterm exam has been posted. 
Week
9 
March
05: Midterm exam preparation March
07: Midterm
exam 


Week
10 
March
12: Midterm exam review March
14: Knowledgebased agents and propositional logics slides 
Section
7.1  7.4 

Week
11 
March
19: Inference with propositional logics slides March
21: Inference with resolution slides 
Section
7.5 Section
7.5 
Exercise
9 has been posted. Homework 4
has been posted, due on Thursday 4/4/2019. Programming
project 3 has been posted, due on Thursday 4/11/2019. 
Week
12 
March
26: NO CLASS (Spring Break) March
28: NO CLASS (Spring Break) 


Week
13 
April
02: Introduction to firstorder logics slides April 03: review session (45PM, BB W475): exercise 9 April
04: Inference with firstorder logics slides 
Section
8.1  8.3 Section
9.1, 9.2, 9.5 
Exercise
10 has been posted. 
Week
14 
April
09: Quantifying uncertainty slides April
11: NO CLASS (EDay) April 12: review session (89AM, BB 280I): exercise 10 
Section
13.1  13.2 

Week
15 
April
16: Inference with probabilistic models slides April 17: review session (45PM, BB W475): exercise 11 April
18: Probability and the Bayesian Networks slides 
Section
13.3  13.6, 14.1 Section
14.2 
Exercise
11 has been posted. Programming
project 4 has been posted, due on Thursday 4/30/2019. Homework 5
has been posted, due on Thursday 4/26/2019. 
Week
16 
April
23: Inference with Bayesian Networks slides April
25: Introduction to learning agents April 26: review session (89AM, BB 280I) 
Section
14.2  14.4 Chapter
18 

Week
17 
April
30: Introduction to deep learning and its application in AI May 1st: review session (45PM, BB W475) May
02: Introduction to deep learning and its application in AI May 04:
Final exam: 10:15am  12:15pm, BB W250 (current classroom) 




Academic integrity
policy
1. Feel free to discuss assignments with each other, but coding and reports
must be done individually.
2. Feel free to incorporate code or tips you find on the Web, provided this
does not make the assignment trivial and you explicitly
acknowledge your sources.
3.
The
collaboration policies for programming projects in all CS courses must be
followed.