1. CS/CMPE 535 – Machine Learning Outline

2. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS2 Description A course on the fundamentals of machine learning – the science of designing and implementing adaptive systems  Concept learning  Inductive learning and decision trees  Computational learning theory  Bayesian learning  Graphical models Emphasis on fundamental mathematical and conceptual understanding Significant exposure to real-world implementations and applications

3. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS3 Goals To provide a comprehensive introduction to machine learning methods To build mathematical foundations of machine learning and provide an appreciation for its applications To provide experience in the implementation and evaluation of machine learning methods To develop research interest in the theory and application of machine learning

4. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS4 Machine Learning is …. Essential for those who want to specialize in artificial intelligence and/or want to pursue research in data mining, machine learning, robotics, computer vision, and computer networks Strongly recommended for all graduate students interested in research Recommended for students with applied sciences backgrounds such as engineering

5. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS5 Before Taking This Course… You should be comfortable with… Probability!  MATH 131 is a prerequisite  Please revise and keep handy the notes from this course Artificial intelligence  General conceptual understanding would be of much help  CS331/CS531 is recommended, not required Programming  MATLAB  C/C++ or Java

6. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS6 Grading Points distribution Quizzes (~ 7)15% Assignments (hand + computer)20% Midterm exam30% Final exam (comprehensive)35%

7. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS7 Policies (1) Quizzes  Most quizzes will be announced a day or two in advance  Unannounced quizzes are also possible Sharing  No copying is allowed for assignments. Discussions are encouraged; however, you must submit your own work  Violators can face mark reduction and/or reported to Disciplinary Committee Plagiarism  Do NOT pass someone else’s work as yours! Write in your words and cite the reference. This applies to code as well.

8. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS8 Policies (2) Submission policy  Submissions are due at the day and time specified  Late penalties: 1 day = 10%; 2 day late = 20%; not accepted after 2 days  An extension will be granted only if there is a need and when requested several days in advance. Classroom behavior  Maintain classroom sanctity by remaining quiet and attentive  If you have a need to talk and gossip, please leave the classroom so as not to disturb others  Dozing is allowed provided you do not snore loud

9. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS9 Policies (3) Attendance  Although attendance is not recorded and graded (in general) it is strongly recommended. Otherwise, you will miss out on key understandings not explicitly covered in the textbook  This recommendation is based on experience of previous courses

10. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS10 Summarized Course Contents Introduction, motivation, and applications Concept learning Decision tree learning Evaluating hypotheses and computational learning theory Bayesian learning Graphical models

11. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS11 Course Material Required textbook  T. Mitchell, Machine Learning, McGraw-Hill, 1997. Recommended supplementary text  E. Alpaydin, Introduction to Machine Learning, Pearson Education, 2004.  C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. Other material  Handouts (papers and tutorials as and when necessary) Other resources  Books in library  Web

12. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS12 Course Web Site For announcements, lecture slides, handouts, assignments, quiz solutions, web resources: http://suraj.lums.edu.pk/~cs535w07/ The resource page has links to information available on the Web. It is basically a meta-list for finding further information.

13. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS13 Other Stuff How to contact me?  Office hours: 11.40 to 13.10 MW (office: 429)  E-mail: akarim@lums.edu.pkakarim@lums.edu.pk  By appointment: to see me outside the office hours e-mail me for an appointment before coming Philosophy  Knowledge cannot be taught; it is learned.  Be excited. That is the best way to learn. I cannot teach everything in class. Develop an inquisitive mind, ask questions, and go beyond what is required.  I don’t believe in strict grading. But… there has to be a way of rewarding performance.

14. CS 535 - Machine Learning (Wi 2007-2008) - Asim Karim @ LUMS14 Reference Books in LUMS Library There are numerous books on machine learning and related topics in the library. Browse the library holdings to get a feel of the books Search the library portal using keywords like “machine learning”, “learning”, “statistical learning”, etc