**Week**
| **Date**
| **Lecture** | **Slides** | **Assignments** |
---|

1 | 1/14 | Course overview | | 0-class-intro.pptx python-setup.pptx | |
---|

| 1/16 | Introduction to classification | | |
---|

2 | 1/21 | Python tutorial | | |
---|

| 1/23 | Decision tree I | | |
---|

3 | 1/28 | Decision tree II | | |
---|

| 1/30 | *Lab 1* | | |
---|

4 | 2/4 | Project idea; Overview of most common techniques, processes, and applications | | |
---|

| 2/6 | Overview of writing and presentation | | Lab 1 due |
---|

5 | 2/11 | Regression I | | |
---|

| 2/13 | Regression II | | Project proposal due |
---|

6 | 2/18 | Regression III | | |
---|

| 2/20 | *Lab 2* | | |
---|

7 | 2/25 | Project discussion: proposal | | |
---|

| 2/27 | Applied data sciences invited talk | | Lab 2 due |
---|

8 | 3/3 | Integration of predictive modeling and domain knowledge: Student presentation 1 | | |
---|

| 3/5 | Integration of predictive modeling and domain knowledge: Student presentation 2 | | |
---|

9 | 3/8-14 | Spring break - No class | | |
---|

10 | 3/17 | Neural network I | | |
---|

| 3/19 | Neural network II | | Project midterm report due |
---|

11 | 3/24 | Neural network III | | |
---|

| 3/26 | *Lab 3* | | |
---|

12 | 3/31 | Project discussion: midterm report | | |
---|

| 4/2 | Applied data sciences invited talk | | Lab 3 due |
---|

13 | 4/7 | Integration of predictive modeling and domain knowledge: Student presentation 3 | | |
---|

| 4/9 | Integration of predictive modeling and domain knowledge: Student presentation 4 | | |
---|

14 | 4/14 | Integration of predictive modeling and domain knowledge: Student presentation 5 | | |
---|

| 4/16 | Integration of predictive modeling and domain knowledge: Student presentation 6 | | |
---|

15 | 4/21 | Project | | |
---|

| 4/23 | Project | | |
---|

16 | 4/28 | Project final presentation | | |
---|

| 4/30 | Project final presentation | | Project final report due |
---|