DSC 240 (Winter 2025) Introduction to Machine Learning

Syllabus [ link ]

Instructor: Prof. Yu-Xiang Wang (homepage)
TA: Mrinaal Dogra: (Email: mdogra@ucsd.edu)

Lecture Section: Tuesday, Thursday 9:30-10:50 am. Location: CENTR 105

Piazza: https://piazza.com/ucsd/winter2025/dsc240_wi25_a00/home
Piazza is our main channel of communication. Questions should be posted here.

Gradescope: https://www.gradescope.com/courses/941346
This is where you submit your homeworks and project reports.

Office hours: Instructor: Tuesday 11:00-12:00 at HDSI 352.
TA office hour to be announced on Piazza.

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

Reference books:

Chris Bishop. Pattern Recognition and Machine Learning, 2006. [Available here]
Zhang, Lipton, Li, Smola. Dive into Deep Learning, Web Edition, 2017. [Available here]

Course Schedule

	Tues and Thurs	Lectures	Reading materials	Project	Homework	Notes
1	7-Jan	Intro and course overview			[HW0, HW0_data]
2	9-Jan	Spam Filtering [Annotated]	Lecture note			[Notebook:NumpyBasics]
3	14-Jan	ML Basics [Annotated]	Lecture note	MP1 Out [StartUpKit]	HW1 Linear Algebra [HW1] / HW0 Due
4	16-Jan	Linear Algebra Review [Annotated]
5	21-Jan	How to train a linear classifier? Perceptron [Annotated]	Bishop 4.1			[Perceptron Proof]
6	23-Jan	Surrogate Loss and First-Order Optimization [Annotated]	D2L 12.3.1, 12.3.2, 12.4.1
7	28-Jan	Linear Regression [Annotated]	D2L 3.1	MP2 out [StartUpKit] / MP1 due	HW2 Linear Regression [HW2, HW2 data] / HW1 Due
8	30-Jan	Regularization [Annotated]	Bishop 3.1.4
9	4-Feb	Generalization theory + Midterm Review [Annotated]
10	6-Feb	Midterm Quiz
11	11-Feb	Max-Margin Linear Separator and Probability Review [Annotated]	Bishop 2.1-2.3
12	13-Feb	Statistics review and Max-Likelihood Estimation [Annotated]	Bishop 4.2.2, Bishop 4.3	MP2 Due	HW3 Naïve Bayes vs Logistic Regression [HW3, HW3 data] / HW2 Due
13	18-Feb	Generative Models and Naive Bayes Classifier [Annotated]	Bishop 4.2.1-4.2.3	MP3 Out [StartUpKit]
14	20-Feb	Decision Tree and Boosting [Annotated]	Bishop 14.2, 14.3, 14.4
15	25-Feb	Feature Expansion and Kernel Methods [Annotated]	Bishop 1.1, 6.1, 6.2
16	27-Feb	Neural Networks [Annotated]	Bishop 5.1, D2L 2.5, D2L 5.1		HW4 Neural Nets, Clustering and PCA[HW4, HW4_code] / HW3 Due	[Notebooks: jax_demo]
17	4-Mar	Unsupervised Learning (Clustering and GMM) [Annotated]	Bishop 9.1, 9.2, (optional 9.3)			Demo: SGD for kmeans
18	6-Mar	Unsupervised Learning (Dimension Reduction) [Annotated]	Bishop 12.1, (optional 12.2)
19	11-Mar	Advanced Topic (Reinforcement Learning) and Final Review [Annotated]		MP3 Due	HW4 Due
20	13-Mar	Final Quiz (9:30 am CENTR 105)

Code of Conduct: You can find UCSD's student code of conduct here.