DSC 240 (Winter 2026) Graduate Introduction to Machine Learning

Syllabus [ link ]

Instructor: Prof. Yu-Xiang Wang (homepage)
TA(s): Zhaner Mou

Lectures: Monday / Wednesday / Friday, 9:00 - 9:50am
Location: COA 125
No lecture on public holidays: Jan 19 (MLK Day), Feb 16 (Presidents’ Day).

Piazza: [link]
Piazza is our main channel of communication. Questions should be posted here.

Gradescope: [link]
This is where you submit homeworks and project reports.

Office hours: Instructor: Wednesday 10-11 at HDSI 352 (Tentative)

Evaluation: 10% each for the three coding projects, 10% each for the top three written homeworks, 20% Midterm, 20% Final.

COMP Exam policy: Pass = score >75% on both the midterm quiz and final quiz.

Reference books

Chris Bishop. Pattern Recognition and Machine Learning, 2006. [Available here]

Zhang, Lipton, Li, Smola. Dive into Deep Learning, Web Edition. [Available here]

Course Schedule

We will cover two modules per week (modules correspond to Winter 2025 lecture units).
Slides will be posted shortly before each lecture session. (Module titles below can later be linked to slides.)
Annotated lectures/notes and homeworks (and any solutions) will be uploaded only after the corresponding module/section is completed.

Week	Dates (MWF)	Modules	Reading materials	Project	Homework	Notes
1	Jan 5, Jan 7, Jan 9	Module 1: Intro and course overview Module 2: Spam Filtering [Annotated]	(no required reading)		HW0, HW0_data	Notebook: NumpyBasics
2	Jan 12, Jan 14, Jan 16	Module 3: ML Basics [annotated] Module 4: Linear Algebra Review[annotated]	(no required reading)	MP1 Out [starter kit ]	HW1 out HW0 Due
3	Jan 19 (No class), Jan 21, Jan 23	Module 5: How to train a linear classifier? Perceptron [annotated]	Module 5: Bishop 4.1 Module 6: D2L 12.3.1, 12.3.2, 12.4.1
4	Jan 26, Jan 28, Jan 30	Module 6: Surrogate Loss and First-Order Optimization [annotated] Module 7: Linear Regression[annotated]	Module 7: D2L 3.1 Module 8: Bishop 3.1.4	MP2 Out [starter kit ]: MP1 Due	HW2 out [HW2 Data] HW1 Due
5	Feb 2, Feb 4, Feb 6	Module 8: Regularization [Annotated] Module 9: Midterm Review [Annotated] Midterm Quiz on Feb 6	(no required reading)		Midterm (Friday in-class quiz)
6	Feb 9, Feb 11, Feb 13	Module 10: Max-Margin Linear Separator and Probability Review [Annotated] Module 11: Statistics review and Max-Likelihood Estimation [Annotated]	Module 10: Bishop 2.1-2.3 Module 11: Bishop 4.2.2, Bishop 4.3	MP2 Due	HW3 out [HW3 Data] HW2 Due
7	Feb 16 (No class), Feb 18, Feb 20	Module 12: Generative Models and Naive Bayes Classifier [Annotated]	Module 12: Bishop 4.2.1-4.2.3 Module 13: Bishop 14.2, 14.3, 14.4	MP3 Out [starter kit ]
8	Feb 23, Feb 25, Feb 27	Module 13: Decision Tree and Boosting [Annotated] Module 14: Feature Expansion and Kernel Methods [Annotated] Module 15: Neural Networks [Annotated]	Module 14: Bishop 1.1, 6.1, 6.2 Module 15: Bishop 5.1, D2L 2.5, D2L 5.1		HW4 out [HW4 Data] HW3 Due	Notebook: jax_demo
9	Mar 2, Mar 4, Mar 6	Module 16: Unsupervised Learning (Clustering and GMM)[annotated] Module 17: Unsupervised Learning (Dimension Reduction) [annotated]	Module 16: Bishop 9.1, 9.2 (optional 9.3) Module 17: Bishop 12.1 (optional 12.2)
10	Mar 9, Mar 11, Mar 13	Module 18: Advanced Topic 1 (Reinforcement Learning) [annotated] Module 19: Advanced Topic 2 (ML theory) [annotated] Module 20: Final Review / Wrap-up [annotated]		MP3 Due	HW4 Due

Week

Dates (MWF)

Modules

Reading materials

Project

Homework

Notes

Jan 5, Jan 7, Jan 9

Module 1: Intro and course overview
Module 2: Spam Filtering [Annotated]

(no required reading)

HW0, HW0_data

Notebook: NumpyBasics

Jan 12, Jan 14, Jan 16

Module 3: ML Basics [annotated]
Module 4: Linear Algebra Review[annotated]

(no required reading)

MP1 Out [starter kit ]

HW1 out
HW0 Due

Jan 19 (No class), Jan 21, Jan 23

Module 5: How to train a linear classifier? Perceptron [annotated]

Module 5: Bishop 4.1
Module 6: D2L 12.3.1, 12.3.2, 12.4.1

Jan 26, Jan 28, Jan 30

Module 6: Surrogate Loss and First-Order Optimization [annotated]
Module 7: Linear Regression[annotated]

Module 7: D2L 3.1
Module 8: Bishop 3.1.4

MP2 Out [starter kit ]: MP1 Due

HW2 out [HW2 Data]
HW1 Due

Feb 2, Feb 4, Feb 6

Module 8: Regularization [Annotated]
Module 9: Midterm Review [Annotated]
Midterm Quiz on Feb 6

(no required reading)

Midterm (Friday in-class quiz)

Feb 9, Feb 11, Feb 13

Module 10: Max-Margin Linear Separator and Probability Review [Annotated]
Module 11: Statistics review and Max-Likelihood Estimation [Annotated]

Module 10: Bishop 2.1-2.3
Module 11: Bishop 4.2.2, Bishop 4.3

MP2 Due

HW3 out [HW3 Data]
HW2 Due

Feb 16 (No class), Feb 18, Feb 20

Module 12: Generative Models and Naive Bayes Classifier [Annotated]

Module 12: Bishop 4.2.1-4.2.3
Module 13: Bishop 14.2, 14.3, 14.4

MP3 Out [starter kit ]

Feb 23, Feb 25, Feb 27

Module 13: Decision Tree and Boosting [Annotated]
Module 14: Feature Expansion and Kernel Methods [Annotated]
Module 15: Neural Networks [Annotated]

Module 14: Bishop 1.1, 6.1, 6.2
Module 15: Bishop 5.1, D2L 2.5, D2L 5.1

HW4 out [HW4 Data]
HW3 Due

Notebook: jax_demo

Mar 2, Mar 4, Mar 6

Module 16: Unsupervised Learning (Clustering and GMM)[annotated]
Module 17: Unsupervised Learning (Dimension Reduction) [annotated]

Module 16: Bishop 9.1, 9.2 (optional 9.3)
Module 17: Bishop 12.1 (optional 12.2)

Mar 9, Mar 11, Mar 13

Module 18: Advanced Topic 1 (Reinforcement Learning) [annotated]
Module 19: Advanced Topic 2 (ML theory) [annotated]
Module 20: Final Review / Wrap-up [annotated]

MP3 Due

HW4 Due