Topics on Artificial Intelligence

CSCI 440, fall semester, 2012


Instructor: Dr. Shieu-Hong Lin       Course Website:

Class:                 M W 3:00-4:15am Business 209

Office Hours: Mon. & Wed. 3:30-5:00pm, Math & CS department


Course objectives:[1] 

P         Gain a general understanding of Artificial Intelligence research and the applications in speech recognition, computer vision, natural language processing, data mining, machine learning, and automatic reasoning through reading and programming assignments.

P         Establish the in-depth understanding of the mathematical and algorithmic framework underlying the very important subject of machine learning.

P         Learn to use software and programming environments such as WEKA for data mining, R for statistical computing, OpenCV for computer vision, or NLTK for natural language processing.  

P         Cultivate the problem solving capability based on the in-depth understanding of machine learning or other selected subject through hands-on study projects using the related software and programming environments.



P         Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Prentice Hall, 2009.

P         I.Witten & E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, 2011.

P         Luis Torgo, Data Mining with R: Learning with Case Studies, Chapman and Hall/CRC, 2011.





Grading Policy

1.Attendance & participation                                                           10%

2.Reading                                                                                            15%

3.Homework& programming assignments                                          45%

4.Project                                                                                              15%

5.Exam                                                                                                15%


Weekly progress reports: They are always due on Wednesdays. Download the template file from the class website. By Wednesday each week, you should spend around 5~10 minutes to add the latest progress in reading and programming made since last Wednesday into the report, and email the file to as an attachment to Dr. Lin.


Project: Youll need to conduct an in-depth project on data mining using WEKA or R, or on computer vision using OpenCV, or on natural language processing using NLTK, or on knowledge representation and automatic reasoning using Eclipse for general game playing.  



Tentative Schedule

P         Week 1          Perspectives of artificial intelligence

P         Weeks 2-4     Speech recognition and spelling recognition

P         Weeks 5-7     Introduction to WEKA and machine learning

P         Weeks 8-10   More on machine learning and data mining using R

P         Week 11                Computer vision and machine learning

P         Week 12        Natural language processing and machine learning

P         Week 13        Knowledge representation: general game playing

P         Week 14        Automatic reasoning: general game playing

P         Week 15        Project presentation


[1] Students desiring accommodations on the basis of physical, learning, or psychological disability for this class are to contact Disability Services.  Disability Services is located in the Learning Center (upstairs in the Biola Library) and cab e reached by calling 562-906+4542 or extension 4542 from campus.)