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

Winter 2023

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• Lectures: TR 3:30pm-4:45pm @ PAB 1749

• Instructor: Arash A. Amini.

  • Office Hours: No office hours.

• Reader: De La Cueva

• Announcements: Will be posted Campuswire (Use code 3748).

• Use Gradescope for homework submission. (Code Y7K5B6)

Exams

• Midterm: Take-home. Posted 2/21 ~ 9pm. Due in 24 hours. (Midterm Statistics)
• Final: Take-home. Posted 3/20 ~ 9pm. Due in 40 hours.

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.

• Homework 1: Due Jan 21, 11pm on Gradescope

Exercise | 1.1 | 1.3 | 1.5 | 2.1 | 2.3 | 2.5 | 2.6 Points | 5 | 5 | 5 | 5 | 10 | 10 | 5

• Homework 2: Due Jan 29, 11pm on Gradescope

Exercise | 2.8 | 2.9 | 2.10 | 2.12 | 2.13 | 2.14 | 2.16 Points | 10 | 10 | 5 | 10 | 10 | 5 | 10

• Homework 3: Due Feb 5, 11pm on Gradescope

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

• Homework 4: Due Feb 12, 11pm on Gradescope

Exercise | 3.7 | 3.9 | 3.10 | 3.11 | 3.17
Points | 15 | 10 | 10 | 10 | 15

• Homework 5: Due Feb 26, 11pm on Gradescope

Exercise | 3.20 | 3.21 | 5.3 | 5.4 | 5.5 | 5.7
Points | 5 | 10 | 10 | 10 | 10 | 15

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

Exercise | 5.10 | 5.11 | 5.12 | 5.13 | 5.19 | 5.20
Points | 5 | 15 | 5 | 10 | 15 | 15

• Homework 7: Due Mar 20, 11pm on Gradescope

Exercise | 6.2 | 6.4 | 6.5 | 6.6 | 7.1 | 7.2
Points | 5 | 10 | 5 | 15 | 15 | 20

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 (?)