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:

Course Schedule

Tues and ThursLecturesReading materialsProjectHomeworkWednesdaysDiscussion Sections
128-SepIntro and course overview  HW0 Self-Assessment  
23-OctSpam Filtering [Annotated]Flach Prologue, 2.1  4-OctMP1 Q&A
35-OctML Basics [Annotated] Lecture noteMP1 OutHW1 Linear Algebra  
410-OctLinear Algebra Review [Annotated]  11-OctLinear Algebra
512-OctHow to train a linear classifier? Perceptron [Annotated]Bishop 4.1, Flach 7.2    
617-OctSurrogate Loss and First-Order Optimization [Annotated]Flach 2.2; D2L 12.3.1, 12.3.2, 12.4.1  18-OctMP2 Q&A
719-OctLinear Regression [Annotated]Flach 3.2, 7.1; D2L 3.1MP2 out / MP1 dueHW2 Linear Regression  
824-OctRegularization [Annotated]Flach 9.1, 9.2  25-OctLinear Regression
926-OctMidterm Review [Annotated]     
1031-OctMidterm   1-NovProbability
112-NovMax-Margin Linear Separator and Probability Review [Annotated] Bishop 2.1-2.3, Flach 9.3  
127-NovStatistics review and Max-Likelihood Estimation [Annotated] Bishop 4.2.2, Bishop 4.3MP3 OutHW3 Naïve Bayes vs Logistic Regression8-NovNaïve Bayes
139-NovGenerative Models and Naive Bayes Classifier [Annotated] Bishop 4.2.1-4.2.3    
1414-NovDecision Tree and Boosting [Annotated]Bishop 14.2, 14.3, 14.4 15-NovHW2 Discussion
1516-Nov Feature Expansion and Kernel Methods [Annotated]Bishop 1.1, 6.1, 6.2, Bishop 5.1    
1621-NovClustering (k-means and GMMs) [Annotated] Bishop 9.1, 9.2, (optional 9.3) HW4 PCA and clustering22-NovHW3 Discussion
1723-NovNo class (Thanksgiving)     
1828-NovDimension Reduction (PCA) [Annotated]Bishop 12.1, (optional 12.2) 29-NovUnsupervised Learning
1930-NovAdvanced Topic [Annotated]      
205-DecFinal Review [Annotated] MP3 DueHW4 Due6-DecHW4 Discussion


In-Class Final Exam