Email: jdwilson4@usfca.edu
Class Time: MWF 10:30 - 11:35 in LS 210
Office Hours: MW 1:30 - 3:00 in Harney 107B
Book: Intro to Statistical Learning (ISL) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Syllabus: Link
By the end of this course, you will be able to implement and mathematically justify the following concepts and methodologies of statistical machine learning
Basic Statistical Concepts
- The learning problem
- Training vs. testing
- Resampling and cross validation
- Model assessment
Supervised Learning
- Regression: principal components regression, Lasso, Ridge regression, Elastic net
- Classification: k-Nearest Neighbors, Linear and Quadratic Discriminant analysis, logistic regression, naive Bayes classifiers, support vector machines
- Tree-based methods: decision trees for regression and classification, bagging, random forests, boosting
- Introduction to neural networks (if time permits)
Unsupervised Learning
- Clustering: k-means, hierarchical clustering, spectral clustering
- Goodness of fit and method assessment
- Biclustering
This course will be assessed according to the following.
- Homework Assignments (35%) For each assignment, you will be required to upload a .pdf file to the Canvas site that contains your R code, any analyses, and any visualization used to answer the questions on the assignment. This .pdf file must be a result of compiling R code in RStudio using the knitr package. These must be submitted before the deadline set on github.
- Case Studies (35%) There will be several in- and out- of class case studies throughout the class. These are to be completed using RStudio.
- Final Exam (30%) The final exam will be comprehensive and cover all material provided in class.
Overall, this course will be split into three main parts: (1) regression, (2) classification, and (3) unsupervised learning and clustering.
Introduction
| Topic | Reading | Assignment | Due Date | In Class Code |
|---|---|---|---|---|
| Introduction | Ch.1, Section 2.1 ISL | Homework 0 | Wednesday, Jan 31st |
Regression
| Topic | Reading | Assignment | Due Date | In Class Code |
|---|---|---|---|---|
| Components of Regression | Section 2.2, Section 3.1 - 3.3, Section 5.1 of ISL | Section 3.3 ISL | ||
| Shrinkage Methods | Section 6.2 of ISL | Shrinkage Code | ||
| Principal Components Regression | 6.3.1 of ISL; A Tutorial on PCA | Homework 1 | Wednesday, Feb 14th | PCA Code |
Classification
| Topic | Reading | Assignment | Due Date | In Class Code |
|---|---|---|---|---|
| Components of Classification | Section 4.1 - 4.3 of ISL | Homework 2 | Friday, March 2nd | |
| kNN and Bayes Classifiers | Section 2.2.3, Section 4.4 of ISL | |||
| LDA, QDA, and Logistic Regression | Section 4.3, Section 4.5 of ISL | Homework 3 | Monday, April 2nd | Classification Code |
Tree-based Methods
| Topic | Reading | Assignment | Due Date | In Class Code |
|---|---|---|---|---|
| Trees, Bagging, and Random Forests | Sections 8.1 and 8.2 of ISL | Ensemble Methods Code | ||
| Boosting | Section 8.2 and 8.3 of ISL |
Unsupervised Learning
| Topic | Reading | Assignment | Due Date | In Class Code |
|---|---|---|---|---|
| k-Means and Hierarchical Clustering | Section 10.1 - 10.3 of ISL | |||
| Graphs and Community Detection |
Intro to Neural Networks and Deep Learning
| Topic | Reading | Assignment | Due Date | In Class Code |
|---|
| Case Study | Date |
|---|---|
| Spam Detection and Naive Bayes | February 23rd |
| Ensemble Methods | April 1st |
- Friday, January 26th - Last day to add the class
- Friday, February 9th - Census date. Last day to withdraw with tuition reversal
- Monday, February 19th - Presidents' Day (no class)
- Monday, March 12th - Friday, March 16th - Spring break! (no class)
- Friday, March 30th - Easter holiday (no class)
- Wednesday, May 9th - Last day of class!
- Monday, May 14th - Final exam (10:00 AM - 12:00 PM)