CSCE 421-500 Machine Learning: Spring 2021


  • [03/18/2021] Homework 2 due postponed 3/22 Monday 11:59pm.
  • [03/12/2021] Weekly schedule updated.
  • [03/05/2021] Homework 2 due postponed 3/18 Thursday 11:59pm.
  • [02/12/2021] Homework 1 due postponed to 2/21 2/28 Sunday 11:59pm.
  • [01/28/2021] Lecture 1 recording has been moved to Canvas->Files->lecture recordings (non-cloud)
  • [01/28/2021] Homework 1 announced. See Canvas->assignments. Download ipynb file from Canvas->Files->assignments
  • ---
  • [01/19/2021] Zoom (lectures): see Canvas
  • [01/12/2021] Welcome to Spring 2021 Machine Learning!

Page last modified: 2/20/21, 10:07AM Saturday.

General Information Resources Weekly Schedule Lecture Notes

I. General Information


Dr. Yoonsuck Choe
Email: choe(a)
Office: HRBB 322B
Phone: 845-5466
Zoom (office hour):
Zoom (lectures): see Canvas
Office hours: Tue/Thu 3pm-4pm (Zoom: see above)

TA and/or Grader:

Qing Wan (TA)
Email: frankwan(a)
Office hours: Wed/Fri 3-4pm.

Ankit Garg (Grader)
Email: ankitgarg(a)
* Graders do not hold office hours.


Grade of C or better in MATH 304 and STAT 211; grade of C or better in CSCE 221 or STAT 404.


TR 8:00am-9:15am, ONLINE, Synchronous (you will receive the zoom link via email)


Machine learning is the study of self-modifying computer systems that can acquire new knowledge and improve their own performance; survey machine learning techniques, which include induction from examples, Bayesian learning, artificial neural networks, instance-based learning, genetic algorithms, reinforcement learning, unsupervised learning, and biologically motivated learning algorithms. Prerequisite: CSCE 420 or CSCE 625.


The goal of this course is to help you to
  1. learn the theoretical foundations of machine learning,
  2. learn various problems and solution strategies in machine learning, and
  3. learn practical methodology for applying ML algorithms to problem domain of your choice.


The expected accomplishments of the students are as follows:
  1. Become confident in the basic mathematics and algorithms underlying machine learning.
  2. Become confident in applying machine learning techniques to solve novel problems.
  3. Become effective in rapidly prototyping machine learning algorithms.
  4. Become effective in designing and conducting experiments with machine learning algorithm, and analyzing the results.

Course Learning Outcomes:

The students who take this course should be able to demonstrate the following upon the completion of this course.
  1. Knowledge of basic mathematics and algorithms underlying various machine learning techniques.
  2. Knowlege of various supervised, unsupervised, and reinforcement learning algorithms.
  3. Ability to implement, from scratch, the above mentioned learning algorithms.
  4. Ability to formulate problems in a machine learning framework, and select the most appropriate machine learning algorithm.
  5. Ability to design and conduct machine learning experiments.
  6. Ability to analyze the results from machine learning experiments.
  7. Appreciation of latest advances in machine learning (including but not limited to biologically motivated, neuroscience-inspired machine learning methods).
  8. Ability to seek more advanced knowledge in machine learning, as needed.
  9. Ability to use Colab for rapid prototyping, experimenting, and documenting.

Textbooks and Resources:

Computer accounts etc.:

  1. The assignments will be done in Google Colab: TAMU NetID required.
  2. Programming languages permitted: Python (basically, it needs to run in colab).

Topics to be covered:

See the Weekly Schedule section for more details. The content will closely reflect a combination of Alpaydin + Mitchell.


  1. 5 assignments (each includes written and programming components), 10% each = 50%.
    • Late policy: 1 point (out of 100 point total) deduction per hour (24/100 per day).
    • All submissions should be done as colab notebooks. Download the ipynb file and submit it to Canvas. After completing each problem in the homework, "pin" the revision. As a separate file, submit the revision logs. Hand-written notes may be included in the notebook (scan or photo).
  2. 2 exams, 20% each = 40%
    • Exam will be conducted on Canvas.
  3. Attendance: 10%
    • Attendance will be checked through zoom log.
    • If you are absent from the class (either late or leave early) for more than 20 minutes, it will count as an absence.
    • First absence will be excused (unconditionally, except for exams).
    • Afterwards, each absence will result in 2% deduction.
    • Note: absences will continue to deduct 2% from your total score, beyond the 10%, up to 30% (by this point, you've missed almost half of the semester).

The cutoff for an `A' will be at most 90% of total score, 80% for a `B', 70% for a `C', and 60% for a `D'.

Local Course Policy:

III. Weekly Schedule and Class Notes

Notices and Dues
1 1/19 Introduction Alpaydin chap 1; Mitchell 1.1–1.2, 1.3–1.5     slide01-intro.pdf
1 1/21       slide01-intro.pdf
2 1/26 Supervised Learning (general) Alpaydin chap 2; Mitchell 7.1–7.2, 7.4     slide02-sup.pdf
2 1/28   Homework 1 announced   slide02-sup.pdf
3 2/2 Multilayer perceptrons Alpaydin chap 11; Mitchell chap 4     slide03-nnet.pdf
3 2/4       slide03-nnet.pdf
4 2/9       slide03-nnet.pdf
4 2/11       slide03-nnet.pdf
5 2/16 No class Class canceled due to weather    
5 2/18 No class Class canceled due to weather (see hw1 tip videos and backprop video on Canvas)    
6 2/23 Reinforcement learning Alpaydin chap 18; Mitchell chap 13 Homework 2 announced   slide04-rl.pdf
6 2/25     Homework 1 due 2/28 Sunday slide04-rl.pdf
7 3/2 Texas Independence Day (No Class) No class    
7 3/4 Reinforcement Learning Alpaydin chap 18; Mitchell chap 13   slide04-rl.pdf
8 3/9 Midterm Exam (in class)        
8 3/11 Reinforcement Learning Alpaydin chap 18; Mitchell chap 13   Homework 2 due slide04-rl.pdf
9 3/16 Decision tree learning Alpaydin chap 9; Mitchell chap 3     slide05-dt.pdf
9 3/18 Redefined day (no class) Students attend friday classes. No class for TR classes. March 19: Spring break Homework 3 announced Homework 2 due Monday 3/22
10 3/23 Decision tree learning Alpaydin chap 9; Mitchell chap 3
10 3/25 Dimensionality reduction Alpaydin chap 6: 6.1–3, 6.7, 6.8     slide06-dim.pdf
11 3/30 Dimensionality reduction Alpaydin chap 6: 6.1–3, 6.7, 6.8     slide06-dim.pdf
11 4/1 Bayesian learning Mitchell chap 6     slide07-bayes.pdf
12 4/6 Bayesian learning Mitchell chap 6 Homework 4 announced Homework 3 due (4/6 Tuesday) slide07-bayes.pdf
12 4/8 Local models Alpaydin chap 12     slide08-local.pdf
13 4/13 Local models Alpaydin chap 12     slide08-local.pdf
13 4/15 Deep learning       slide09-dl.pdf
14 4/20 Deep learning       slide09-dl.pdf
14 4/22 Deep learning       slide09-dl.pdf
15 4/27 Deep learning       slide09-dl.pdf
15 4/29 Advanced topics See Canvas-files-lecture slides; Support Vector Machine slide (informational purposes only)   Homework 4 due May 1 (Sat) slide10-svm.pdf
5/5 (Wed)Final Exam (5:00pm-7:30pm)   

IV. University Policy

Attendance Policy:

The university views class attendance and participation as an individual student responsibility. Students are expected to attend class and to complete all assignments.

Please refer to Student Rule 7 in its entirety for information about excused absences, including definitions, and related documentation and timelines.

Also, see local course policy regarding attendance.

Makeup Work Policy

Students will be excused from attending class on the day of a graded activity or when attendance contributes to a student's grade, for the reasons stated in Student Rule 7, or other reason deemed appropriate by the instructor.

Please refer to Student Rule 7 in its entirety for information about makeup work, including definitions, and related documentation and timelines.

Absences related to Title IX of the Education Amendments of 1972 may necessitate a period of more than 30 days for make-up work, and the timeframe for make-up work should be agreed upon by the student and instructor (Student Rule 7, Section 7.4.1).

The instructor is under no obligation to provide an opportunity for the student to make up work missed because of an unexcused absence (Student Rule 7, Section 7.4.2).

Students who request an excused absence are expected to uphold the Aggie Honor Code and Student Conduct Code. (See Student Rule 24.)

Academic Integrity Statement and Policy:

AGGIE HONOR CODE: An Aggie does not lie, cheat, or steal or tolerate those who do.

Texas A&M University students are responsible for authenticating all work submitted to an instructor. If asked, students must be able to produce proof that the item submitted is indeed the work of that student. Students must keep appropriate records at all times. The inability to authenticate one's work, should the instructor request it, may be sufficient grounds to initiate an academic misconduct case (Section, Student Rule 20).

You can learn more about the Aggie Honor System Office Rules and Procedures, academic integrity, and your rights and responsibilities at

If there are any questions or concerns about whether an action is appropriate, you should check with the professor or teaching assistant first. If in doubt, assume that it is not appropriate.

Americans with Disabilities Act (ADA) Policy

Texas A&M University is committed to providing equitable access to learning opportunities for all students. If you experience barriers to your education due to a disability or think you may have a disability, please contact Disability Resources in the Student Services Building or at (979) 845-1637 or visit Disabilities may include, but are not limited to attentional, learning, mental health, sensory, physical, or chronic health conditions. All students are encouraged to discuss their disability related needs with Disability Resources and their instructors as soon as possible.

Title IX and Statement on Limits to Confidentiality

Texas A&M University is committed to fostering a learning environment that is safe and productive for all. University policies and federal and state laws prohibit gender-based discrimination and sexual harassment, including sexual assault, sexual exploitation, domestic violence, dating violence, and stalking.

With the exception of some medical and mental health providers, all university employees (including full and part-time faculty, staff, paid graduate assistants, student workers, etc.) are Mandatory Reporters and must report to the Title IX Office if the employee experiences, observes, or becomes aware of an incident that meets the following conditions (see University Rule 08.01.01.M1):

Mandatory Reporters must file a report regardless of how the information comes to their attention including but not limited to face-to-face conversations, a written class assignment or paper, class discussion, email, text, or social media post. Although Mandatory Reporters must file a report, in most instances, you will be able to control how the report is handled, including whether or not to pursue a formal investigation. The University's goal is to make sure you are aware of the range of options available to you and to ensure access to the resources you need.

Students wishing to discuss concerns in a confidential setting are encouraged to make an appointment with Counseling and Psychological Services (CAPS)

Students can learn more about filing a report, accessing supportive resources, and navigating the Title IX investigation and resolution process on the University's Title IX webpage.

Statement on Mental Health and Wellness

Texas A&M University recognizes that mental health and wellness are critical factors that influence a student's academic success and overall wellbeing. Students are encouraged to engage in proper self-care by utilizing the resources and services available from Counseling and Psychological Services (CAPS). Students who need someone to talk to can call the TAMU Helpline (979-845-2700) from 4:00 p.m. to 8:00 a.m. weekdays and 24 hours on weekends. 24-hour emergency help is also available through the National Suicide Prevention Hotline (800-273-8255) or at

Campus Safety Measures

To promote public safety and protect students, faculty, and staff during the coronavirus pandemic, Texas A&M University has adopted policies and practices for the Fall 2020 academic term to limit virus transmission. Students must observe the following practices while participating in face-to-face courses and course-related activities (office hours, help sessions, transitioning to and between classes, study spaces, academic services, etc.):

Personal Illness and Quarantine

Students required to quarantine must participate in courses and course-related activities remotely and must not attend face-to-face course activities. Students should notify their instructors of the quarantine requirement. Students under quarantine are expected to participate in courses and complete graded work unless they have symptoms that are too severe to participate in course activities.

Students experiencing personal injury or Illness that is too severe for the student to attend class qualify for an excused absence (See Student Rule 7, Section 7.2.2.) To receive an excused absence, students must comply with the documentation and notification guidelines outlined in Student Rule 7. While Student Rule 7, Section, indicates a medical confirmation note from the student's medical provider is preferred, for Fall 2020 only, students may use the Explanatory Statement for Absence from Class formin lieu of a medical confirmation. Students must submit the Explanatory Statement for Absence from Class within two business days after the last date of absence.

Last edited:2/20/21, 10:07AM (Sat)