CSCI 4336 -- Introduction to Machine Learning

CSCI 5931 -- Research Topics in Computer Science - Machine Learning
Updated February 23, 2021

COVID-19 Related Information

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Syllabus Changes
Due to the changing nature of the COVID-19 pandemic, please note that the instructor may need to make modifications to the course syllabus and may do so at any time. Notice of such changes will be announced as quickly as possible via email to your UHCL account and your GMail account.

Recording of Class
Students may not record all or part of class, livestream all or part of class, or make/distribute screen
captures, without advanced written consent of the instructor. If you have or think you may have a
disability such that you need to record class-related activities, please contact the Accessibility

Support Center. If you have an accommodation to record class-related activities, those recordings

may not be shared with any other student, whether in this course or not, or with any other person or on any other platform. Classes may be recorded by the instructor. Students may use instructor’s recordings for their own studying and note taking. Instructor’s recordings are not authorized to be shared with anyone without the prior written approval of the instructor. Failure to comply with requirements regarding recordings will result in a disciplinary referral to the Dean of Students Office and may result in disciplinary action.

Face Covering Policy
To reduce the spread of COVID-19, UHCL requires face coverings on campus including classrooms for
both faculty and students. Face coverings must cover your mouth and nose and be worn throughout the class session. A mask with a valve is not considered an adequate face covering and should not

be used, as it can expel exhaled air, increasing the risk to others. Eating or drinking during class is discouraged and is not an excuse for removing the face covering for any extended length of time. Failure to comply with the requirement to wear a face covering in class will result in your being asked

to leave the classroom immediately and a disciplinary referral through the Dean of Students Office. Exceptions will also be made for those individuals who, due to a specific medical condition,

cannot wear a face covering and have received an accommodation. Requests for an exception due a medical condition for students will be handled by the Accessibility Support Center.

Required Daily Health Self-Assessment
Your presence in class each session means that you have completed a daily self-assessment of your
health/exposure and you:

• Are NOT exhibiting any Coronavirus Symptoms

• Have NOT tested positive for COVID-19

• Have NOT knowingly been exposed to someone with COVID-19 or suspected/presumed COVID-19

If you are experiencing any COVID-19 symptoms that are not clearly related to a pre-existing medical
condition, do not come to class. Please complete COVID-19 Report of Diagnosis/Symptoms. If you
believe you may have been exposed please complete COVID-19 Report of Exposure.

Helpful Links:
COVID-19 Updates:

        https://www.uhcl.edu/health-alert/
Online Learning Assistance and Reimbursement Program (OLARP):

        https://www.uhcl.edu/dean-ofstudents/emergency-assistance/online-learning-assistance

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Office and Addresses

Delta 171 Phone 281.283.3805
email: boetticher@uhcl.edu
Secretary: Ms. Caroline Johnson, Delta 161 281.283.3860

Class Hours (Face-to-Face or Online)

Tuesday 2:30 - 3:50, Online
Thursday 2:30 - 3:50, Online

Office Hours

Tuesday 1:30 to 2:30 PM; Wed. 4 to 7 PM; Thurs 12:30 to 2:30 PM, or by appointment. Students with appointments have priority. If the suite door is locked, then call my extension (last 4 digits) using the phone in the hallway. Students who have an appointment will have priority over those students who don't. During the COVID-19 situation, you will be emailed a Zoom link.

Teaching Assistant

TBD
Email: TBD

TA Hours: TBD

     Zoom details available on the Google Drive.

Blackboard link

Course Description

Introduction to concepts of machine learning: elements of probability distributions and linear algebra, supervised and unsupervised learning, linear and nonlinear regression, classification, neural networks, support vector machines, sampling methods, K-Means clustering, Bayesian networks, and reinforcement learning. Applicability of each technique will be discussed.

Course Goals

• Understand the difference between supervised and unsupervised learning

• Understand the concepts regression and classifications

• Be able to implement the different algorithms and apply them to real data.

• Understand and apply neural networks.

• Understand Support Vector Machines (SVM)

• Understand and apply reinforcement learning techniques.

• Apply these techniques in some of the most recent challenges such as cyber security, IoT, and
  data science.

Learning Outcomes:

By the end of the course, you will:

1. Be able to understand how machine learning is applied in many real world problems.
2. Be able to explain how regression, classification, and neural networks are applied.
3. Be able to use machine learning-supporting to prototype solutions prior to deploying them into applications.
4. Be able to apply probabilistic inference techniques.
5. Understand the applicability of reinforcement learning techniques.
6. Understand how SVM and K-Means clustering is used.

Prerequisites

CSCI 2315 (Data Structures), knowledge of statistics.

Methodology

Lecture, seminar, case studies, and interactive problem solving.

Appraisal:

Grades will be based solely on criteria listed above. No other factors will be considered. Below are some of factors that will not be considered:

• Expected a higher grade
• Good course participation
• Good improvement during the semester
• Have put in extra effort
• Need to avoid probation
• Financial needs
• Loss of scholarship
• Loss of job opportunity
• Loss of practical training opportunity
• Need to graduate
• Company relocation
• Immigration status needs
• Family needs
• Sickness during the semester

Grading Scale

    93+ = A; 90 = A-; 87+ = B+; 83+ = B; 80+ = B-;

      77+ = C+; 73+ = C; 70 = C-; 67+ = D+; 63+ = D; 60+ = D-; 0+= F

My motto:

Foster disciplined, altruistic passion.

Required Textbook

None.

Reference Books

1. Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin. Learning from data. Vol. 4. New York, NY, USA::   AMLBook, 2012. http://www.amlbook.com.

2.  Berry and Linoff, Data Mining Techniques, Wiley, 2000.

3.  Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006

4. Géron, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, 2019.

5.  Mitchell, Tom, Machine Learning, McGraw-Hill, Boston, 1997.

6.  Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.

Schedule (Tentative)

Assign Homework 1

Point value: 100 points

Due date:  Tuesday, February 9^th, 2:30 PM

FOR THIS WEEK (IF NOT SOONER)       

            Blue Color = Available on the Google Drive

                It is the student's responsibility to download the notes, print the notes, and bring them to class.

·   Read:  Syllabus

·   Quiz:  Complete Quiz 00 - Syllabus and Online Orientation. The due date is Feb. 4, 2021 at 2:30 PM.

·   Read documents in:  WK01 Notes - What is Data Mining and the DM Process.zip

·   Quiz:  Complete Quiz 01 - What is Data Mining and the DM Process. The due date is Feb. 4, 2021 at 2:30 PM.

·   Read documents in:  WK02 Notes - The Data in Data Mining.zip

·   Quiz:  Complete Quiz 02 - The Data in Data Mining. The due date is Feb, 4, 2021 at 2:30 PM.

Jan 26/Jan 28 – The Data in Data Mining, General Types of Data Mining Problems

Feb 02/Feb 04 – The Experimental Process

Feb 09/Feb 11 – Linear Regression, Logistic Regression, Lasso Regression, Ridge Regression

Assignment 1 is due. Tuesday February 9^th, 2:30 PM

Assign Homework 2

Point value: 100 points

Due date:  Tuesday, February 23^rd, 2:30 PM

Feb 14 – Numpy, Jupyter, Keras, Tensorflow = Sunday FunDay Session

Feb 16/Feb 18 – Classes cancelled due to weather conditions

Feb 23/Feb 25 – Deep Learning: Neural Networks: Perceptron, Backpropagation

Assignment 2 is due. Thursday, February 25^th, 2:30 PM

Mar 02/Mar 04 – Deep Learning: Convolutional Neural Networks, Assignments 3 and 4

Assign Homework 3 and 4

Point value: 100 points each

Proposal Due date:  Tuesday, March 23^rd, 2:30 PM

Mar 09 – Review

Mar 11 – Midterm

Mar 16 – Spring Break

Mar 18 – Spring Break

Mar 23/Mar 25 – Deep Learning: Generative Adversarial Networks (GANs)

Assignment 3/4 Proposals are due. Tuesday March 23^rd, 2:30 PM

Mar 30/Apr 01 – Reinforcement Learning (RL)

Apr 06/Apr 08 –  Decision Trees

April 13th - Last day to drop

Apr 13/Apr 15 – Support Vector Machines

Apr 20/Apr 22 – Ensemble Learning

Assignment 4 is due. Thursday April 22^nd, 2:30 PM

Apr 27/Apr 29 – Presentations, Review

May 04 – Final Exam

Other Policies

Homework, Projects, Research Paper

• Homework and projects are due exactly at the prescribed time (usually the beginning of class). As soon as a homework or project is collected, then all others are considered 1 day late (even if it only 3 minutes). In the event you might be running late, you might want to email the assignment. Also, when preparing your assignment, be mindful of possible backlogs at the printer, jammed printer, printer out of toner, etc.

• Late homework/projects are accepted with a penalty of 10% deduction per 24-hour period after the due date. No late project will be accepted one week after the due date. The last homework/project cannot be late.

• There will be no extra-credit homework or projects in this course.

• All homework and projects must be typed not hand-written.

• A cover page is expected for all homework and projects.

• VERY IMPORTANT! In certain classes students are encouraged to work in groups. For this class you are expected to work on all homework and projects individually for most assignments. Students may not discuss, use, email, show, give, buy, sell, borrow, trade, steal, download from the Internet, etc. in whole or part, any of the homework or projects in any manner not prescribed by the instructor. This condition applies even after you complete this course! Penalty for cheating will be extremely severe and will result at least a one letter grade reduction in your final grade. It could result in an F for this course. Cheating can cost result in losing a scholarship, a TA position, or an RA position.  There may be some group assignments for this class. If there is inappropriate sharing among two or more groups, then all students will be considered guilty. Choose your partner very carefully!

• Handing in an assignment for another student is considered cheating. Penalty for cheating will be extremely severe and may result in an F for this course. 

• VERY IMPORTANT! Failing to report to the instructor any incident in which a student witnesses an alleged violation of the Academic Honesty Code is considered a violation of the academic honesty code. Please see me to discuss any incidents.

• VERY IMPORTANT! Purchasing, or otherwise acquiring and submitting as one's own work any research paper or any other writing assignment prepared by others constitutes cheating. Penalty for cheating will be extremely severe and may result in an F for this course.

• VERY IMPORTANT! Plagiarism on either an abstract, draft of a paper, or final paper will result in a 0 for all three parts (abstract, draft version, final paper). Please review the following links regarding plagiarism very carefully: https://www.indiana.edu/~istd/definition.html

• http://www.hamilton.edu/style/avoiding-plagiarism

• http://www.writing.utoronto.ca/advice/using-sources/how-not-to-plagiarize

• Standard academic honesty procedure will be followed. For the UHCL Academic Honesty Policy, please click on the following link.

Tests and Quizzes

• There are no make-up tests except in verified medical emergencies and with immediate notification. Rescheduling a final exam in order to catch a plane flight in order to go back home without a significant reasons and corresponding documentation is unacceptable. Make up exams are harder and different from the original exams.

• There are no make-up quizzes. Allow plenty of additional time in the event that Blackboard crashes.

• You are responsible for all required readings assigned throughout the semester.

• Students are to work on test and quizzes individually.  Students may not discuss, show, give, sell, borrow, trade, share, etc. their tests or quizzes. Penalty on cheating will be extremely severe. Standard academic honesty procedure will be followed.

• VERY IMPORTANT! Providing answers for any assigned work or examination when not specifically authorized by the instructor to do so. Or, informing any person or persons of the contents of any examination prior to the time the examination is given is considered cheating. Penalty for cheating will be extremely severe and may result in an F for this course.

• VERY IMPORTANT! Failing to report to the instructor any incident in which a student witnesses an alleged violation of the Academic Honesty Code is considered a violation of the academic honesty code. Please see me to discuss any incidents.

Disability Accommodations:

Students with special needs and disability should contact the instructor as soon as possible and contact Disability Services Office at 281-283-2627 website: www.uhcl.edu/disability
 

6 Drop Rule:

Students who entered college for the first time in Fall 2007 or later should be aware of the course drop limitation imposed by the Texas Legislature. Dropping this or any other course between the first day of class and the census date for the semester/session does not affect your 6 drop rule count. Dropping a course between the census date and the last day to drop a class for the semester/session will count as one of your 6 permitted drops. You should take this into consideration before dropping this or any other course. Visit www.uhcl.edu/records for more information on the 6 drop rule and the census date information for the semester/session.

Miscellaneous

• Any person with a disability who requires a special accommodation should inform me and contact the Disability services office or call 281 283 2627 as soon as possible.

• You are expected to come fully prepared to every class!

• Incomplete grades or administrative withdrawals occur only under extremely rare situations.

• You need to bring a hard copy of the notes to class. Laptops will be permitted only during software demos such as WEKA.

• For all lecture-based classes, please turn off your laptops.

• The ringing, beeping, buzzing of cell phones, watches, and/or pagers during class time is extremely rude and disruptive to your fellow students and to the class flow.

  Also, sending and/or receiving text messages during class is extremely rude and disruptive. Please turn off all cell phones, watches, and pagers prior to the start of class.

  If I see (even if the cell phone is off) or hear a cell phone during class, or see a student texting during class,   then 3 points will be deducted for each infraction from your final course average.

• Attendance Policy:

  Face-to-face: You are expected to attend every class. If you miss more than 1 class, then your course grade will be reduced by 2 points for each lecture missed. Coming late to class on a regular basis will impact your course participation grade.

   

  Pure Web-based: You do not need to attend any lectures on campus. Also, you do not need to show up in  person to take the exams. However, you may attend any/all of the face-to-face lectures and/or exams. However, it is my experience that those students who do attend class on a regular basis do better on tests than those that don't. If you will be off-campus during the exams, please make the necessary arrangements with me as soon as possible.

• I am willing to provide letters of recommendation/references only if you have attained an 'A' in one of my classes, or two 'A-' in two of my classes.

• I highly recommend that you seek out your advisor and complete you Candidate Plan of Study (CPS) as soon as possible. I am normally not available for advising during the summer months.

• Pay very careful attention to your email correspondence. It reflects on your communication skills. Below is a compilation of email errors I have received during the past year.

  Dear boeticher,

  Is there any chance of regrading my final grade. As i'am very nervous in exam i couldn't be able to attempt properly. you know how attentive in class and can u please grade me considering my class participation also or do i have a chance of re exam because c grade draws my gpa low which results in loosing my scholorship, Please consider my request.

  Thanks and Regards

  Some Student

  Common problems:

     *   bcoz instead of because

     *   r instead of are

     *   u instead of you

     *   lowecase i instead of I

     *   starting a sentence with a lowercase letter

     *   doubt instead of question

   

• I immediately discard anonymous emails.

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