Course Info: CS-0342
| Course | CS-0342 Machine Learning |
| Long Title | Machine Learning |
| Term | 2019S |
| Note(s) |
Instructor Permission Required Prerequisites Required Textbook information |
| Meeting Info | Cole Science Center 316 on T,TH from 2:30-3:50 |
| Faculty | Ethan Meyers |
| Capacity | 16 |
| Available | 4 |
| 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 | Machine learning is a subfield of artificial intelligence that aims to give computers the ability to make predictions and find relationships in data. The methods used in machine learning blend statistical concepts with ideas from computer science, and are widely used by data scientists to analyze complex datasets, and by artificial intelligence researchers to make intelligent systems. This class covered the central concepts in machine learning including regression, supervised learning (classification), unsupervised learning (clustering and dimensionality reduction), cross-validation methods, and model selection. The Python programming language was used to gain experience applying these methods to on real data sets. The class work consisted of 6 programming worksheet problem sets and a midterm and final project. |