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: Thursdays 11am-1pm (starting 1/18/24).
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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.
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Grading: Attendence 5%, Homework 30%, Midterm 30%, Final 35%.
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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 (?)