STA 9890 - Course Syllabus

All syllabus and course schedule provisions subject to change with suitable advance notice.

Instructor

Course Meetings

Lecture / Lab Sessions

Office Hours

  • In-Person
  • Virtual:
    • Thursdays 4:30pm-5:30pm
    • Zoom link provided via Brightspace

Grading

STA 9890 is a(n advanced) master’s-level course in Statistical Machine Learning. As such, by the end of the course, students should be able to capably apply standard machine learning techniques to important scientific and business applications, to extend and adapt standard machine learning techniques in novel fashions, to implement complex estimation and prediction algorithms, and to critically engage with modern machine learning literature. The grading in this course reflects this diversity of objectives.

Specifically, the grading in this course draws on three separate modes of assessment: i) in-class ‘midterms’ which assess students’ fluency with the theoretical and mathematical aspects of machine learning; ii) three ‘research reports’ designed to build students’ skills in the computational and methodological aspects of machine learning; and iii) a course prediction competition, designed to build students’ skills in applications of machine learning to important scientific problems. All three elements are equally weighted as follows:

  • 33.3% Mid-Semester Tests (Best two of three; 100 points each, 200 points total)
    • Test 1: 2025-03-04 - Theory of Supervised Learning; Regression
    • Test 2: 2025-04-08 - Classification; Ensemble Learning; Tree-Based Methods
    • Test 3: 2025-05-06 - Unsupervised Learning
  • 33.3% Research Reports (Best two of three; 100 points each, 200 points total)
    • Report 1: Due 2025-03-07
    • Report 2: Due 2025-04-18
    • Report 3: Due 2025-05-09
  • 33.3% Prediction Competition (200 points total)

Final course grades will be curved in accordance with relevant program, departmental, school, and college policies.1

Weekly Quizzes

In lieu of homework, I will provide a short list of practice problems and suggested readings following each lecture. At the start of the following lecture, a short three question (\(\approx\) 15 minute) quiz will be administered. The quiz questions will not be verbatim from the practice problems, but if you can answer the practice problems quickly and fluently, the quiz should pose little difficulty.

These in-class weekly quizzes will generate extra credit applied to your final aggregate score. Each quiz will receive a score out of 3 added directly to your final score. Because the final aggregate score is out of 600, perfect scores on all 9 weekly quizzes can raise your final aggregate score (pre-curve) up to 5%.

To take part in the weekly quizzes, please come to class with both i) a black or blue pen; and ii) a red pen (for peer grading) each week.

Make-up opportunities for the weekly quizzes will only be allowed in exceptional and unforeseeable circumstances.

Regrading Policy

If you feel an assignment has been improperly graded, please contact the instructor by private message on the course discussion board within 48 hours of the graded assignment being returned. Note that the instructor will regrade the assignment de novo, so your grade may be adjusted upwards or downwards.

Late Work Policy

Students are given 4 Late Days that can be used at any time during the semester, without instructor pre-approval or permission, on any of the submitted elements of this course (competition final report, research reports). When late days are used, work is accepted without penalty; late work submitted beyond the four late days will not be accepted. Late Days are used on a “first come, first serve” basis, so if 2 Late Days are used on the first two research reports, any late submission on the final research report will not be accepted.

Outside of the use of Late Days, no late work will be accepted. Late Days cannot be applied to in-class activities such as tests or presentations.

Individual exceptions will be made i) with prior written approval of instructor; or ii) ex post with written note from the Office of the Dean of Students.

Attendance Policy

Attendance is not required for this course and absences do not need to be excused. Make-up opportunities for Quizzes and Tests missed while absent will not provided except with i) with prior written approval of instructor; or ii) ex post with written note from the Office of the Dean of Students.

Pre- and Post-Reading Suggestions

Students learn material most effectively when exposed to it on multiple occasions, ideally using alternative presentations strategies and formats.2 To this end, suggested pre-reading and post-reading is provided for each week of the course. Students are encouraged to pre-read the recommended text, which typically presents that week’s material in a less technical / more intuitive manner, before each week’s course session. Similarly, students are encouraged to review the post-reading for each week after lecture to see additional examples of topics covered.

While lectures will focus primarily on ‘big picture’ and ‘major themes’, the recommended reading, especially the post-reading, provides additional coverage of relevant technical detail. Note that pre- and post-reading are entirely optional and will not be directly assessed. I have included (overlapping) presentations from various text books to encourage students to find the style and presentation that works best for them.

Tentative Course Schedule

Week Lecture Date Topics Pre-Reading Post-Reading Research Reports Additional Notes

1

2025-01-28

Course Overview & Introduction to ML:

  • Taxonomy of Learning
  • In-Sample and Out-of-Sample Accuracy
  • Complexity and Overfitting
  • Nearest Neighbor Methods

None

ISL §2

HR §1-4

UML §1-2, 5, 19

2

2025-02-04

Regression I:

  • Review of OLS
  • Linear Algebra Review
  • Introduction to Convex Optimization

DFO §2.1-2.6, §3.1-3.4, §5.1-5.2, §5.8, §7.1, §7.3, §9.1-9.2

ISL §3

BV §2-4

HR §5, 14

UML §12, §14

Research Report #01 released on 2025-02-04 - due on 2025-03-07

3

2025-02-11

Regression II:

  • Regularization
  • Ridge Regression
  • Sparsity and the Lasso
  • Supervised Model Selection

DFO §8.1-8.3

ISL §6

BV §6

SLS §2, 4, 5

PML-1 §11.1-11.4

UML §11, 13, 25.1
2025-02-18 No class: Baruch on Monday Schedule (President’s Day Make-Up)

4

2025-02-25

Regression III:

  • Non-Linear Methods
  • Kernel Trick
  • Splines

DFO §12.4

ISL §7

PML-1 §11.5-11.8, §17.1

UML §16
5 2025-03-04 Mid-Term Test I: Regression
Introduction to Classification

6

2025-03-11

Classification I:

  • Basics of Classification
  • Mixture Methods & Generative Methods

DFO §11.1-11.5

ISL §4

PML-1 §10, 12

UML §9, 24

Research Report #01 Due on 2025-03-07 (NB - Friday before class)

Research Report #02 released on 2025-03-11 - due on 2025-04-18

7

2025-03-18

Classification II:

  • Discriminative Methods
  • Multi-Class Classification
  • Fairness in Machine Learning

DFO §12.1-12.6

ISL §9

SLS §3

PML-1 §9

UML §15

8

2025-03-25

Ensemble Learning & Resampling Methods

ISL §5

SF §1-3, 5

UML §10

9

2025-04-01

Tree-Based Methods

ISL §8

PML-1 §18

UML §18
10 2025-04-08 Mid-Term Test II: Classification, Ensemble Learning, Tree-Based Methods
Introduction to Unsupervised Learning
2025-04-15 No class: Baruch Spring Recess

11

2025-04-22

Unsupervised Learning I

  • Dimension Reduction
  • Principal Components Analysis
  • Covariance Estimation

DFO §3.5, §3.8, §4.2-4.6, §10.1-10.8

ISL §12.1-12.3

SLS §7-8

PML-1 §20.1-20.2

UML §23.1

Research Report #02 Due on 2025-04-18 (NB - Friday before class)

Research Report #03 released on 2025-04-22 - due on 2025-05-09

12

2025-04-29

Unsupervised Learning II

  • Clustering
  • Density Estimation
  • Outlier Detection
  • Manifold Learning

ISL §12.4-12.5

PML-1 §20.3-21.6

UML §22
13 2025-05-06 Mid-Term Test III: Unsupervised Learning
Introduction to Generative Models
14 2025-05-13 Course Project Presentations
Course Wrap-Up 		Research Report #03 Due on 2025-05-09 (NB - Friday before class)

Workload Expectations

The following approximate breakdown of expected course workload is intended to help you properly prepare for and schedule the out-of-class work associated with this course. Note that, persuant to relevant Federal and State regulations, a 3-credit course taken over a 15 week semester should require approximately 6 hours of out-of-class work from an average student, or 90 hours total over the course of the semester.34 This time should be roughly allocated as:

  • Weekly Pre-Reading (9 hours - 1 hour per lecture)
  • Weekly Post-Reading and Review (18 hours - 2 hours per lecture)
  • Test Preparation (15 hours total - 5 hours per test)
  • Research Reports (27 hours total - 9 hours each)
  • Prediction Competition (21 hours total)
    • Ongoing Submissions (20 hours - approximately 2 per week)
    • Final Presentation Preparation (1 hour)

Note that, for the course prediction competition, you will need to make regular progress throughout the semester. If you attempt to “back-load” your work, you will do poorly.

Coding Requirements

STA 9890 is, at its heart, a machine learning course and as such use of a machine, i.e. coding, is required, even though there is no formal coding prerequisite for this course. Per the External Resources Policy, you are allowed (and encouraged) to use freely available coding assistance technologies, including generative tools like GitHub CoPilot.5

You may use whatever programming language you prefer to complete the course assignments, subject to instructor approval: R, python, julia, and matlab are pre-approved for all students by default. (I will approve most other languages as well, provided they are not too obscure.)

Tools like quarto or Jupyter Notebooks will be useful for completing the Research Reports required for this course. If you have not used these previously, many useful free resources can be found online, including my STA9750 course materials.

All syllabus and course schedule provisions subject to change with suitable advance notice.

Footnotes

  1. Theoretically, this may result in scores equivalent to an A in an un-curved course receiving a lower grade in this course. In practice, the instructor will design course assessments to induce a range of scores and does not anticipate “down-curving” happening.↩︎

  2. Haoyu Chen and Jiongjiong Yang. “Multiple Exposures Enhance Both Item Memory and Contextual Memory over Time”. Frontiers in Psychology 11. November 2020. DOI:10.3389/fpsyg.2020.565169↩︎

  3. For this course, an average student is a student who enters the course with:

    1. Fluency with statistical and numerical software at the level of (at least) STA 9750
    2. Fluency with univariate and multivariate regression at the level of (at least) STA 9700
    3. Familiarity with probability and linear algebra

    and is earning a B-range grade. If you have less background or are aiming for a higher grade, you should expect to commit proportionally more time to this course.↩︎

  4. The CUNY Graduate Center has a useful summary of these expectations. Baruch courses follow the same standards.↩︎

  5. As a student, you have free access to GitHub CoPilot once you create a student GitHub account and register for the Student Developer Pack.↩︎