Date | Assignments | Lecture Topics and Slides | Readings, Notes, and Demos | Recitation Topics & Materials |
Wed 01/16 | out: HW0 out: survey0 | Course Overview [slides]
| Demo: Intro to Jupyter Notebooks and Data Analysis with NumPy
| |
Mon 01/21 | due: survey0 | NO CLASS (MLK Holiday) |
| Intro to Python
|
Wed 01/23 | due: HW0 out: HW1 | Regression Overview [slides]
| Read: ISL textbook
Demo [notebook]: Using sklearn’s fit and predict functions for KNeighborsRegressor and DecisionTreeRegressor | |
Mon 01/28 |
| Linear Regr. Algorithms [slides] | Read: ISL textbook
Read: ESL textbook
Read: DL textbook | Review of Matrix Math & Derivatives behind Linear Regression [Math primer from Harvard cs181] [External demo of gradient descent] |
Wed 01/30 | due: HW1 out: HW2 | Regression: Regularization [slides]
| Read: ISL textbook
| |
Mon 02/04 | Statistical Decision Theory [slides]
| Read: ESL textbook
| Pipelines and Cross Validation | |
Wed 02/06 | due: HW2 out: HW3 | Classification Overview [slides]
| Read ISL textbook
Read: EMLM book | |
Mon 02/11 | Logistic Regression [slides]
| Read: ISL textbook
| Logistic Regression numerical implementation issues [notebook] [useful blog post] | |
Wed 02/13 | due: HW3
| Logistic Regression [slides]
| Read: ESL textbook
Read: DL textbook | |
Mon 02/18 |
| NO CLASS (President's Day Holiday) |
| No Recitation (Holiday) |
Wed 02/20 | Feature Processing & Selection [slides]
| Read: ISL textbook
| ||
Thu 02/21 | (Mon on Thurs at Tufts) Neural Networks 1/2 [slides]
| Read: DL textbook Ch 6
| ||
Mon 02/25 |
| Neural Networks 2/2 [slides]
| Skim: M. Nielson textbook
Skim: DL textbook | Neural Nets demo with automatic differentiation [notebook] |
Wed 02/27 | out: HW4 | Classifiers Using Bayes Theorem [slides]
| Read: ISL textbook
Skim: Naive Bayes article by Jake VanderPlas | |
Mon 03/04 | Decision Trees [slides]
Proj 1 work time! | Read: ISL textbook
| Office hours for Project 1 and/or | |
Wed 03/06 | due:Project1 | Review Session for Midterm |
| |
Mon 03/11 |
| Midterm Exam |
| No Recitation |
Wed 03/13 | due: HW4 | Improving Classifier Performance [slides]
Hyperparameter Optimization
| Read: DL textbook
| |
Mon 03/18 | NO CLASS (Spring Break) |
| No Recitation | |
Wed 03/20 | NO CLASS (Spring Break) |
| ||
Mon 03/25 | out: Project2 | Bagging & Boosting [slides]
| Read: ISL textbook
Skim: ESL textbook
| No Recitation |
Wed 03/27 | Kernels [slides]
|
| ||
Mon 04/01 | Support Vector Machines [slides]
| Read: ISL textbook
| ||
Wed 04/03 | out: HW5 | Recommendation Systems [slides]
| Koren et al. 2009. “Matrix Factorization Techniques for Recommender Systems.” [IEEE link] | |
Mon 04/08 | Dim. Reduction: Overview [slides]
| Read: ISL textbook
| ||
Wed 04/10 | due: Project2 due: HW5 | Principal Components Analysis [slides]
| Skim: DL textbook
Applications: | |
Mon 04/15 | NO CLASS (Patriots' Day) |
| Practical SVD and PCA | |
Wed 04/17 | out: Project3 | Clustering: Overview [slides] | Read: Jake VanderPlas’ Data Science Handbook: | |
Mon 04/22 | out: HW6 | Fairness/Ethics in ML [slides] | Examples of ML in the Wild: | |
Wed 04/24 | ML Frontiers
| |||
Mon 04/29 | due: HW6 | Final Exam Review
|
| Final Exam Review |
Fri 05/03 | Final Exam
|
| ||
Wed 05/08 | due: Project3 |