Hello, if you have any need, please feel free to consult us, this is my wechat: wx91due

Statistics 154/254: Modern Statistical Prediction and Machine Learning

Description

• Instructor: Song Mei (songmei [at] berkeley.edu)
• Lectures: Tuesday/Thursday 9:30-11:00. Etcheverry 3108.
• Office Hours: TBA. 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: TBA.

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

1. First class: Aug 29, 2024 (Thursday).
2. For students who filled in the enrollment appeal forms and concurrent enrollment students: I will process all the petitions on Aug 30. Please come to the first few lectures and decide whether you will take this course.
3. We will use Ed for discussions and questions.
4. Please find homework and lecture notes on bCourse under “Files”.
5. 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.
6. 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 6-7 HWs.
• In class mid-term: TBA.
• Final exam date: Dec 17.
• Final grade will be Homework × 40 % + mid-term × 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

• 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.

• Statistical learning theory and optimization theory.
• Bagging and Boosting. Tree based models. Neural networks. Bayesian models.
• Online learning. Bandit problems.