Description

Instructor: Song Mei (songmei [at] berkeley.edu)

Lectures: Tuesday/Thursday 9:30 am  11:00 am. Etcheverry 3108.

Office Hours: Tuesday 1 pm  3 pm. Evans 387.

GSI: Ruiqi Zhang (rqzhang [at] berkeley.edu)

Lab sessions Friday 11:00 am  12:59 pm, Evans 334; 3:00 pm  4:59 pm, Evans 342.

Office Hours: Thursday 4 pm  6 pm; Friday 1 pm  3 pm. Evans 428.
This course will focus on statistical/machine learning methods, data analysis/programming skills. Upon completing this course, the students are expected to be able to 1) build baseline models for real world data analysis problems; 2) implement models using programming languages; 3) draw insights/conclusions from models.
Announcement

First class: Aug 29, 2024 (Thursday).

I will process the CE students during the week of Sep 9. I will prioritize CE students performing well in HW1.

We will use Ed (https://edstem.org/us/courses/61329) for discussions and questions.

Please submit HW using Gradescope (https://www.gradescope.com/courses/811006, code: YRD5EG).

Please find homework and lecture notes on bCourse under “Files”.

HW policy: There are in total 3 late days that you can use without penalty towards grade throughout the semester. After that, there will be a 10% deduction on grades of a HW for each late day. The least grade can be dropped counting towards total grades.

The lectures will be recorded through Course Capture. The recordings can be found on bCourse under “Media Gallery”.
Grading

Class attendance is required.

Homework per two weeks. There will be 67 HWs.

In class midterm: Oct 24. Logistics: https://docs.google.com/document/d/1VfW1isSq68IwW8TQnZF_3o_SxJ0Yy0sDzI8QVeaSSSw/edit?usp=sharing

Final exam date: Dec 17. Logistics: https://docs.google.com/document/d/1VfW1isSq68IwW8TQnZF_3o_SxJ0Yy0sDzI8QVeaSSSw/edit?usp=sharing

Final grade will be Homework × 40 % + midterm × 25 % + final × 35 %.

HW policy: There are in total three late days that you can use without penalty towards grade throughout the semester. After that, there will be a 10% deduction on grades of a HW for each late day. The least grade can be dropped counting towards total grades.
Topics
Basic topics:

Tasks: Regression. Classification. Dimension reduction. Clustering.

Algorithms: Solving linear systems. Gradient descent. Newton’s method. Power iteration for eigenvalue problems. EM algorithms.

Others: Kernel methods. Regularization. Sample splitting. Resampling methods. Cross validation.
Advanced topics:

Statistical learning theory and optimization theory.

Bagging and Boosting. Tree based models. Neural networks. Bayesian models.

Online learning. Bandit problems.
Schedule
Week 1
Week 2
Week 3
Week 4
Week 5
Week 5
Week 6
Week 7
Oct 15: 
Lecture 13 TBA 

Oct 17: 
Lecture 14 TBA 

Week 8
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