STATS C161/C261 - Introduction to Pattern Recognition and Machine Learning

Winter 2024

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• Lectures: TR 9:30am-10:45am @ GEOLOGY 3656

• Instructor: Arash A. Amini.

  • Office Hours: No office hours.

• Reader: Harrison Katz

• Announcements: Will be posted Campuswire (Use code 9938).

• Use Gradescope for homework submission. (Code 5J4NGX)

Exams

• Midterm: Take-home. Date: Will be posted on Feb 20 at noon. Due on Feb 21 at 9pm on Gradescope.
• Final: Take-home. Date: TBA

Please read!

Please do not email me your late homework. Instead, post a note on Campuswire (it can have attachments) and set the visibility to TAs and Instructors only.

• Grading: Attendence 5%, Homework 30%, Midterm 30%, Final 35%.

• Prerequisites:

  • STATS 100B
  • Linear Algebra such as Math 33A
  • Coding: R and/or Python
  • Optional but recommended: STATS 100C (Linear Models)
  • Optional but highly recommended: Multivariate Gaussian Distribution

Homework and slides

• Homework and slides will be posted in the following Box folder.

The homework problems are from the lecture notes which can be found on the Box folder.

• Homework 1: Due Jan 20, 11pm on Gradescope

Exercise | 1.1 | 1.3 | 1.5 | 2.3 | 2.4 | 2.6 | 2.8 Points | 5 | 5 | 5 | 10 | 10 | 10 | 5

• Homework 2: Due Jan 28, 11pm on Gradescope

Exercise | 2.7 | 2.8 | 2.10 | 2.13 | 2.15 | 2.16 Points | 10 | 5 | 10 | 10 | 9 | 10

• Homework 3: Due Feb 8, 11pm on Gradescope

Exercise | 3.2 | 3.3 | 3.4 | 3.5 | 3.6
Points | 10 | 10 | 10 | 10 | 10

• Homework 4: Due Feb 18, 11pm on Gradescope

Exercise | 3.7 | 3.9 | 3.10 | 3.14 | 3.18 | 3.21 Points | 15 | 10 | 5 | 10 | 15 | 5

• Homework 5: Due March 4, 11pm on Gradescope

Exercise | 6.5 | 6.6 | 6.7 | 6.8 | 6.9 | 6.10 | 6.13
Points | 5 | 10 | 10 | 5 | 10 | 15 | 10

• Homework 6: Due Mar 11, 11pm on Gradescope

Exercise | 6.14 | 6.17 | 6.18 | 6.24 | 6.27
Points | 11 | 5 | 10 | 12 | 10

Lecture videos

• Lecture videos are on this playlist.

Textbook(s)

There is no official textbook for this course. I will provide lecture notes and slides. But here is a list of possible textbooks you can chose from:

• An Introduction to Statistical Learning, James, Witten, Hastie, Tibshirani, 2nd Ed.
• The Elements of Statistical Learning, Hastie, Tibshirani, Friedman, 2nd Ed.
• Pattern Recognition and Machine Learning, Bishop.
• Introduction to Machine Learning, Alpaydin.
• Dive into Deep Learning
• Applied Predictive Modeling
• A Course in Machine Learning Daumé III.

Syllabus

• Probablistic prediction
• Local averaging
• Empirical risk minimization
• Kernel methods
• Deep learning
• Boosting and bagging
• Unsupervised learning: PCA, etc.
• Clustering (?)
• Reinforcement Learning (?)