CS165B (Fall 2023) Introduction to Machine Learning

Syllabus [ link ]

Instructor: Prof. Yu-Xiang Wang

TA: Esha Singh (esingh AT ucsb.edu)
TA: Ruiquan Li (rli667 AT ucsb.edu)

Lecture: Tuesday/Thursday 5:00-6:15 pm Location: CHEM 1171

Discussion Section 1: Wednesday 1:00-1:50 pm Location: PHELP 2524

Discussion Section 2: Wednesday 2:00-2:50 pm Location: GIRV 1112

Ed Discussion: https://edstem.org/us/courses/48177/discussion//
Edstem is our main discussion forum. Questions should be posted here.

Gradescope: https://www.gradescope.com/courses/632349
This is where you submit your project code and reports.

Office hours: Instructor: After Thursday Lecture until 7:00
TA office hours:

Ruiquan, Monday 2:00 - 3:00, Space in front of Henley Hall 2118

Esha, Wednesday 3:30 - 4:30, Space in front of Henley Hall 2118

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

Reference books:

Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, 1st Edition by Peter Flach, First Edition, 2012
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	Wednesdays	Discussion Sections
1	28-Sep	Intro and course overview			HW0 Self-Assessment
2	3-Oct	Spam Filtering	Flach Prologue, 2.1			4-Oct	MP1 Q&A
3	5-Oct	ML Basics	Lecture note	MP1 Out	HW1 Linear Algebra
4	10-Oct	Linear Algebra Review				11-Oct	Linear Algebra
5	12-Oct	How to train a linear classifier? Perceptron	Bishop 4.1, Flach 7.2
6	17-Oct	Surrogate Loss and First-Order Optimization	Flach 2.2; D2L 12.3.1, 12.3.2, 12.4.1			18-Oct	MP2 Q&A
7	19-Oct	Linear Regression	Flach 3.2, 7.1; D2L 3.1	MP2 out / MP1 due	HW2 Linear Regression
8	24-Oct	Regularization	Flach 9.1, 9.2			25-Oct	Linear Regression
9	26-Oct	Midterm Review
10	31-Oct	Midterm				1-Nov	Probability
11	2-Nov	Max-Margin Linear Separator and Probability Review	Bishop 2.1-2.3, Flach 9.3
12	7-Nov	Statistics review and Max-Likelihood Estimation	Bishop 4.2.2, Bishop 4.3	MP3 Out	HW3 Naïve Bayes vs Logistic Regression	8-Nov	Naïve Bayes
13	9-Nov	Generative Models and Naive Bayes Classifier	Bishop 4.2.1-4.2.3
14	14-Nov	Decision Tree and Boosting	Bishop 14.2, 14.3, 14.4			15-Nov	HW2 Discussion
15	16-Nov	Feature Expansion and Neural Networks	Bishop 1.1, 6.1, 6.2, Bishop 5.1
16	21-Nov	Clustering (k-means and GMMs)	Bishop 9.1, 9.2, (optional 9.3)		HW4 PCA and clustering	22-Nov	HW3 Discussion
17	23-Nov	No class (Thanksgiving)
18	28-Nov	Dimension Reduction (PCA)	Bishop 12.1, (optional 12.2)			29-Nov	Unsupervised Learning
19	30-Nov	Advanced Topic
20	5-Dec	Final Review		MP3 Due	HW4 Due	6-Dec	HW4 Discussion
21	7-Dec	In-Class Final Exam