Course Info: CS-0342
| Course | CS-0342 Statistical Learning |
| Long Title | Introduction to Statistical Learning |
| Term | 2015F |
| Note(s) |
Instructor Permission Required Prerequisites Required Textbook information |
| Meeting Info | Adele Simmons Hall 126 on M,W from 4:00-5:20 |
| Faculty | Ethan Meyers |
| Capacity | 25 |
| Available | 14 |
| Waitlist | 0 |
| Distribution(s) | |
| Cumulative Skill(s) | Quantitative Skills |
| Additional Info | In this course, students are expected to spend at least six to eight hours a week of preparation and work outside of class time. This time includes reading, writing, and research. |
| Description | This class covered central concepts in statistical learning including regression, supervised learning and unsupervised learning. Methods discussed in this class included: multiple regression, logistic regression, the lasso, ridge regression, decision trees, boosting, support vector machines, neural networks, principal component analysis, and hierarchical cluster. We also discussed conceptual ideas including the generalization error and the bias variance trade-off. Student also used the R programming language to explore the effectiveness of different methods and to draw insights from real data sets. The class work consisted of weekly worksheets, which had programming and conceptual problems (8 in total) and a midterm and final project and presentation (5 and 10 page data analysis papers). |