CSE 158: Web Mining and Recommender Systems
Autumn 2019, Monday/Wednesday 17:00-18:20, Galbraith Hall
CSE 158 is an undergraduate course devoted to current methods for recommender systems, data mining, and predictive analytics. No previous background in machine learning is required, but all participants should be comfortable with programming (all example code will be in Python), and with basic optimization and linear algebra.
The course meets twice a week on Monday/Wednesday evenings, starting September 30. Meetings are in Galbraith Hall.
There is no textbook for the course, though chapter references will be provided from Pattern Recognition and Machine Learning (Bishop), and from Charles Elkan's 2013 course notes. Links are also provided to our Coursera Specialization, which covers similar material.
I'll hold office hours on Tuesdays 9:30-13:00 in CSE 4102. The course TAs will hold additional office hours as follows:
- Monday 15:00-17:00: CSE B270A
- Tuesday 14:00-15:00: CSE B215
- Tuesday 15:00-16:00: CSE B270A
- Friday 11:00-13:00: CSE B215
- Homework 1: due Oct 14
- Homework 2: due Oct 28
- Midterm: Nov 6
- Homework 3: due Nov 11
- Assignment 1: due Nov 18
- Homework 4: due Nov 25
- Assignment 2: due Dec 2
- Each Homework is worth 8%. Your lowest (of four) homework grades is dropped (or one homework can be skipped).
- The Midterm is worth 26%.
- Each Assignment is worth 25%.
- Assignment 2 is a group assignment. All other assessment must be completed individually.
- All assessments are due before the Monday lecture on the due date. Late submissions are not accepted.
Textbook and additional reading:
Supervised Learning: Regression
Monday September 30 / Wednesday October 2:
- Least-squares regression
- Overfitting and regularization
- Training, validation, and testing
Coursera slides (introductory):
Supervised Learning: Classification
Monday October 7 / Wednesday October 9:
- Logistic regression
- Multiclass and multilabel classification
- How to evaluate classifiers
Dimensionality Reduction and Clustering
Monday October 14 / Wednesday October 16:
- Principle Component Analysis
- K-means & hierarchical clustering
- Community detection
|Assignment||Assignment 1 (due November 18)|
|Homework||Homework 3 (due November 11)|
|Assignment||Assignment 2 (due December 2)|
|Homework||Homework 4 (due November 25)|
|No lecture|| November 11 (Veteran's Day)|
|No lecture|| November 27 (Thanksgiving)|