CSC872: Pattern Analysis and Machine Intelligence

Fall 2020 (#8497, 3units)

Instructor: Dr. Kazunori Okada

Lec. Session

Tue: 4:00 - 6:45 pm

Lec. Location

Online (see iLearn)

Office Phone

(415) 338-7687

Office

Online (see iLearn)

Office Fax

(415) 338-6826

Office Hours

Wed: 12:00 - 1:00 pm

Email Address

kazokada@sfsu.edu

Web Page

http://online.sfsu.edu/~kazokada/

Mailing Address

Computer Science Department, San Francisco State University
1600 Holloway Avenue San Francisco, CA 94132-4163

Teaching Assistant

Luis Chumpitaz

lchumpit@mail.sfsu.edu

TA Office Hour

TA Office

Wed: 4:00-5:00pm

Online (see iLearn)

 

Lecture Plan (subject to change)

Week

Sub

Topic: Lecture

Topic: Exercise

Notes

Readers

Assignments

Dues

01:08/25

INT

Introduction:

PAMI Frameworks

Project

Discussion

Note01

Exer01

Ch.1

Final

Project

 

02:09/01

AI

Agent-based AI

Framework

MATLAB

Exercise 1

Note02

Exer02

Ch.2

 

 

03:09/08

AI

Problem Solving:

Search Methods

MATLAB

Exercise 2

Note03

Exer03

Ch.3-4

HW 1

(lec01-03)

 

04:09/15

AI

Knowledge Rep.: Propositional Logic

MATLAB

Exercise 3

Note04

Exer04

Ch.7-8

 

 

05:09/22

AI

Knowledge Rep:

First-Order Logic

Fast Prototype 1:

Modeling: PCA 1

Note05

Exer05

Paper 1

Ch.9

HW 2

(lec04-05)

 

06:09/29

PR

Bayesian

Framework

Fast Prototype 1:

Modeling: PCA 2

Note06

Exer06

Ch.13-14, 20

 

 

07:10/06

PR

Statistical Modeling:

Non-Parametric

Fast Prototype 1:

Modeling: PCA 3

Note07

Exer07

Ch.20

HW 3

(lec06-07)

 

08:10/13

PR

Statistical Modeling: Parametric

Fast Prototype 2:

Segmentation: MS 1

Note08

Exer08

Paper 2

Ch.20

 

 

09:10/20

PR

Statistical Modeling:

Mixture Models

Fast Prototype 2:

Segmentation: MS 2

Note09

Exer09

 

HW 4

(lec08-09)

P-Topic

(10/20)

10:10/27

ML

Machine Learning Framework

Fast Prototype 2:

Segmentation: MS 3

Note10

Exer10

Ch.14,

18

 

 

11:11/03

ML

Supervised Learning:

Classification

Fast Prototype 3:

Classify: LDA 1

Note11

Exer11

Paper 3

Ch.3

HW 5

(lec10-11)

 

12:11/10

ML

Supervised Learning:

Regression

Fast Prototype 3:

Classify: LDA 2

Note12

Exer12

Ch.3

 

 

13:11/17

NN

Neural Network:

Functional Learning

Fast Prototype 3:

Classify: LDA 3

Note13

Exer13

Ch.20

 

 

11/24

THANKSGIVING RECESS

-----

----------

------------

 

14:12/01

NN

Neural Network:

Deep Learning 1

Neural Network:

Deep Learning 2

Note14

 

 

 

15:12/08

CON

Project

Final Presentation 1

Project

Final Presentation 2

Pres01

Pres02

 

 

Final-R

(12/8)

 

Basic Information

 

Course Summary:

This course offers an introduction to the modern artificial intelligence: the pattern analysis and machine intelligence (PAMI) studies.  This research field ranges over a wide variety of well-established subjects, including artificial intelligence (AI), pattern recognition (PR), machine learning (ML), neural network (NN), all of which are intricately related to each other because of the shared underlying concepts and theories.  Collectively they contribute to various practical applications, such as graphics/animation, games, factory automation, robotics, video analysis/security, medical imaging, bioinformatics, data mining, to name a few.  The main goal of this course is to develop an intuitive understanding of the various fields, as well as to understand differences between them due to specific historical and application domain biases.  Through lectures together with hands-on prototyping exercises, you will learn not only about a number of fundamental and useful PAMI techniques but also lessons on how to successfully conduct a research project.

 

Objectives:

·       Learn the foundation of PAMI studies:

o   Artificial intelligence

o   Pattern recognition

o   Machine learning

o   Neural network and Deep Learning

·       Learn the general concepts across various fields:

o   Data and knowledge representation

o   Problem formulation

o   Problem solving

·       Become familiarized with basic algorithms in PAMI practice.

·       Become exposed to various application domains and basic literature in PAMI studies.

 

Prerequisites:

          Graduate standing or consent of the instructor.

 

Zoom Lectures:

Lectures will be given via Zoom during the class meeting time. Access to the zoom lecture is given to you in the iLearn page of this course. You are requested to keep the video on during the lectures so that the instructor can see you. Microphones are muted by default and maybe activated by the instructor when required. You may participate discussion and ask questions via Zoom’s chat. Instructors will address them as soon as possible during the lectures. Please prepare your work environment with a computer with zoom client installed and working video camera and microphone. Some zoom lectures will be recorded and made available later for aiding your study in your iLearn page. Attendance for each zoom lecture will be recorded by using iLearn’s attendance function manually. Passcode for self-reporting will be issued at the beginning of each lecture.

 

Office Hours:

Zoom links for office hours for both the instructor and the TA are given in iLearn page. Office hours will be available for online conversations during the chosen office hour time specified above.

 

 

Final Project

 

Literature Survey Report & Presentation:

An independent literature survey project on deep learning is to be carried out by each student. This assignment provides you a hands-on exercise for conducting literature review and presentation toward preparing your own thesis and publication. Each student must choose appropriate representative articles (approved by the instructor before Oct 20), conduct short presentation (on Dec 8), and submit a survey report due on the last class meeting (on Dec 8).  Your work will be graded based on the quality and completeness of your presentation and report. Late policy specified below will apply. Read the assignment in the above lecture plan table for more details.

 

 

In-Class Exercises

 

ZOOM:

All in-class exercises explained below will be given by using Zoom. Please refer to the details described above for using Zoom.

 

MATLAB Tutorials/Exercises:

These tutorials/exercises offer you opportunity to learn MATLAB: a popular numerical computing software tool which will be used during our fast prototyping exercises. The instructors will introduce you to basics of the tool and guide you through some hands-on exercises.  Please prepare your own laptop with an installation of MATLAB. A free copy of MATLAB is available for all SFSU students via https://at.sfsu.edu/at-mathworks-matlab. Please consult the instructor upon questions about this tool. Tutorials given via zoom will be recorded and made accessible as your study resource in the iLearn page when they become available.

 

Fast Prototyping Exercises:

These in-class hands-on programming exercises are designed to help you learn how to implement and test an algorithm quickly, and to familiarize you with practical computer vision applications and their solutions. Three classic algorithms (PCA for face recognition, Mean Shift for clustering/segmentation, and LDA for classification) will be implemented by you. One reference paper will beb provided for each algorithm to familiarize you with their theory. The instructor will provide data, followed by a brief description of tasks to be tackled.  Students are to complete a software prototype during class meetings with minimum preparation.  Please bring your own laptop with an installation of MATLAB. Please refer to the above description on how to acquire the MATLAB copy.

 

 

Exams/Homework

 

Exams:

No midterm/final examinations. J

 

Homework:

Five homework assignments will be given as take-home exams. Their schedule is given in the above lecture plan table.  You will be asked to answer questions and solve quantitative analytical problems. Some homework involves solving difficult pen&paper analytical problems. You are advised not to procrastinate. Unless specified otherwise, each homework is due in one week. 

 

iLearn Usage: 

Distribution and submission of your assignments will be handled through the iLearn course page. After the instructor made it available, click the specified iLearn link to view each homework assignment. Work on your problems and write down your answers on your own papers (make sure you write your name and SID# for each page). After you complete it, scan your answers by using a PDF-Scan tool (e.g., CamScanner, Scannable, GeniusScan, AdobeScan, Multifunction Printer, etc) and save it to a single PDF file, combining all pages into one file. After checking that all pages are present and readable, submit your PDF file by using the specified iLearn assignment submission link.

 

 

Grading Policy

 

Numerical Grade Weights:

·       50%:         Homework

·       25%:         Final Report

·       10%:         Final Presentation

·       15%:         Fast Prototyping

 

Grading Policy:

·       If you like to appeal your homework or test grades, you must do so within one week after graded assignments were made available to students. There will be no exception even if you miss those classes and/or announcements. You are responsible to find out your own grades.

·       The default grade distribution is as follows: A (100% - 92.5%), A- (92.4%-90%), B+ (89.9% - 87.5%), B (87.4% - 82.5%), B- (82.4% - 80%), C+ (79.9% - 75%), C (74.9% - 65%), C- (64.9% - 60%), D+ (59.9% - 57.5%), D (57.4% - 52.5%), D- (52.4% - 50%), F (49.9% - 0%).

 

Late Policy:

·       Late submission is not allowed in general unless an agreement due to documented special circumstance was reached with the instructor before the due date.

·       Late submission for the project proposal and report will be penalized by 10% per day up to 40%.

·       After 4 days, late final report receives zero credit.

 

 

Course Materials

 

Course Web Page:

          https://bidal.sfsu.edu/~kazokada/csc872/

 

Text Book:

Artificial Intelligence: A Modern Approach (4th, 3rd, 2nd Ed), Russell SJ and Norvig P, Prentice Hall, 2009, 2002

            http://aima.cs.berkeley.edu/

 

Recommended Readers:

Pattern Classification (2nd Ed), Duda RO, Hart PE, Stork DG. Wiley-Interscience, 2000 (PR, ML, NN)

            http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html

Neural Network for Pattern Recognition, Bishop CM. Oxford University Press, 1996 (NN, PR, ML)

            http://portal.acm.org/citation.cfm?id=525960

The Elements of Statistical Learning, Hastie T, Tibshirani R, Friedman JH. Springer, 2003 (ML)

            http://www.springer.com/computer/ai/book/978-0-387-84857-0

Digital Image Processing (3rd, 2nd Ed), Gonzalez RC, Woods RE. Prentice Hall, 2007, 2002 (Imaging)

http://www.imageprocessingplace.com/DIP-3E/dip3e_main_page.htm

 

 

Rules

 

Syllabus is Subject to Change:

This syllabus and schedule are subject to change. The official syllabus will be maintained at the course website. It is your responsibility to check on the site frequently and check on announcements made while you were absent.

 

Absence:

Regular attendance is recommended. In the event of an absence, it is the student’s responsibility to learn of any material missed, including announcements made by the instructor. Lectures and demonstrations will not be repeated during office hours. In case of extraordinary circumstance, it is the student’s responsibility to inform the instructor and submit supporting documents as soon as s/he can.

 

In-Class Communication Device Rule:

Any ringing devices such as cell phones and must be turned-off during the lectures. Students with a ringing device will be asked to leave the class meeting immediately and not allowed to come back for the class meeting.

 

Academic Integrity & Plagiarism:

Academic Integrity refers to the “integral” quality of the search for knowledge that a student undertakes. Plagiarism is a form of cheating or fraud; it occurs when a student misrepresents the work of another as his or her own. I encourage discussion among students, but I expect each student to hand in original work. You are responsible for doing your own work and for ensuring that your work is protected from copying. Violation to the university and departmental rules (found in below links) is a serious offence and can result in severe penalties. It is your responsibility to familiarize yourself with the following rules:

·       SFSU Code of Student Conduct: http://conduct.sfsu.edu/standards

·       Academic Dishonesty: http://conduct.sfsu.edu/academic-dishonesty

·       Plagiarism: http://conduct.sfsu.edu/plagiarism 

·       Computer Science Department Policy: https://cs.sfsu.edu/cheating-plagiarism-policy

 

 

Important Resources

 

COVID-19 and Our Campus:

Your health and safety is our paramount concern at SF State. During the COVD-19 pandemic, every member of our Gator community is expected to do their part in keeping fellow students, faculty, and staff safe and well. Feeling well and safe will support you in focusing on your academic success. For the limited number of classes meeting face-to-face, In-person class attendance is an option, but not a requirement. Students who do not wish to or are unable to comply with these requirements will be allowed to take the class virtually or provided with other remote options for course completion Please consult the campus plan website (https://news.sfsu.edu/campus-plan) for up-to-date information and explanation of requirements. For all students attending in-person, the following are required:

1.     Wear a face covering when around other people outside of those in your household.

2.     Stay at least 6 feet physically distant from people outside the members of your household.

3.     Stay home if you have one or more symptoms of COVID-19 (Please check in with the SF DPH website for the most up-to-date symptoms & testing: https://www.sfcdcp.org/wp-content/uploads/2020/04/GetTestedSF-Eng-052920.pdf)

4.     If you would like to discuss reasonable accommodations based on disability related to COVID-19, please contact the Disability Programs & Resource Center: dprc@sfsu.edu

Information is changing rapidly, as our health professionals, scholars, and researchers are learning more about COVID-19, and as such, we encourage you to frequently check your San Francisco State University email account and https://news.sfsu.edu/campus-plan/students-families for the most current information.

·     You are encouraged to keep your emergency information updated on Campus Solutions in order to receive campus emergency alerts: https://upd.sfsu.edu/ENSFAQ

·     You are also encouraged to provide your contact information to receive city of SF emergency alerts, including COVID-19 updates and instructions for public safety: https://sfdem.org/get-cityalerts

·     If you have any questions regarding COVID-19 or your own health during this time, please reach out to Student Health Services: https://health.sfsu.edu

·     If you are feeling overwhelmed, you are encouraged to connect with our on-campus health professionals in Counseling & Psychological Services: https://caps.sfsu.edu

·     If you are looking for education on how to keep yourself and your loved ones healthy, then reach out to our Health Promotion & Wellness Team: https://wellness.sfsu.edu

Learning Assistances:

The Tutoring and Academic Support Center (TASC) is a new university-wide center that supports the academic success of all San Francisco State students. They are offering services online via Zoom. Please email tutoring@sfsu.edu from your SF State email address with your name, student ID, course for which you are seeking tutoring, and available days/times for an appointment. They will reply with details for your online appointment. More information can be found at https://ueap.sfsu.edu/tutoring.

·          Phone Number: 415-405-5516

·          Location: Tutoring is currently being offered online via Zoom

·          Hours: Mon-Thr, 9:00 a.m. to 7:00 p.m. and Fri, 9:00 a.m. to 2:00 p.m.

·          Email: tutoring@sfsu.edu

 

Religious Holidays:

Reasonable accommodations will be made for you to observe religious holidays when such observances require you to be absent from class activities. It is your responsibility to inform the instructor during the first two weeks of class, in writing, about such holidays.

 

Disability Access:

Students with disabilities who need reasonable accommodations are encouraged to contact the instructor.  The Disability Programs and Resource Center (DPRC: https://access.sfsu.edu/) is available to facilitate the reasonable accommodations process. The DPRC is located in the Student Service Building and can be reached by telephone (voice/415-338-2472, video phone/415-335-7210) or by email (dprc@sfsu.edu).

 

Student Disclosures of Sexual Violence:

SF State fosters a campus free of sexual violence including sexual harassment, domestic violence, dating violence, stalking, and/or any form of sex or gender discrimination.  If you disclose a personal experience as an SF State student, the course instructor is required to notify the Title IX Coordinator by completing the report form available at http://titleix.sfsu.edu, emailing vpsaem@sfsu.edu or calling 338-2032.

To disclose any such violence confidentially, contact:

·       The SAFE Place - (415) 338-2208; http://www.sfsu.edu/~safe_plc/

·       Counseling and Psychological Services Center - (415) 338-2208; http://psyservs.sfsu.edu/

·       For more information on your rights and available resources: http://titleix.sfsu.edu

 

Kazunori Okada © 2020, All rights are reserved.