Schedule of Events
Class meetings are held on Tuesdays, Wednesdays, and Thursdays.
On Wednesdays, workshops are 3:00 - 3:50pm in Dawson Hall, Room 0310.
On Tuesdays and Thursdays, lectures are 2:20 - 3:35pm in Journalism, Room 0501.
| Date | Topic | Links |
| Tues, 1/7 | Lecture 1: Welcome to Data Science II | pptx | pdf |
| Wed, 1/8 | Workshop 0: Setting up your Python Environment | materials |
| Thurs, 1/9 | Lecture 2: Python Crash Course | ipynb | html |
| Thurs, 1/9 | Homework 1 Released | pdf | handout |
| Tues, 1/14 | Lecture 3: Machine Learning (with Python) Crash Course | ipynb | html |
| Wed, 1/15 | Workshop 1 (Nathan McEntire, Heeya Jolly): Jupyter notebooks and JupyterLab | |
| Thurs, 1/16 | Lecture 4: Linear Algebra Crash Course (recommend Lessons 1-3) | github | mybinder |
| Tues, 1/21 | ||
| Wed, 1/22 | ||
| Thurs, 1/23 | Lecture 5: Dense Motion Analysis | pptx | pdf |
| Tues, 1/28 | Lecture 6: Linear Dynamical Systems | pptx | pdf |
| Tues, 1/28 | Homework 1 Due; Homework 2 Released | pdf | handout |
| Wed, 1/29 | Workshop 3: Toplogical Data Analysis | |
| Thurs, 1/30 | Lecture 7: Linear and Ensemble Models | pptx | pdf |
| Tues, 2/4 | Lecture 8: Graphs | pptx | pdf |
| Wed, 2/5 | Workshop 4: Seaborn | |
| Thurs, 2/6 | Lecture 9: Spectral Clustering | pptx | pdf |
| Tues, 2/11 | Lecture 10: Semi-supervised Learning | pptx | pdf |
| Tues, 2/11 | Homework 3 Released | pdf | handout |
| Wed, 2/12 | Workshop 5: Keras and TensorFlow | |
| Thurs, 2/13 | Lecture 11: Embeddings I | pptx | pdf |
| Thurs, 2/13 | Homework 2 Due | |
| Tues, 2/18 | Lecture 12: Embeddings II | pptx | pdf | video |
| Wed, 2/19 | Workshop 6 | |
| Thurs, 2/20 | Guest Lecture, Meekail Zain: Statistics You Should've Been Taught | github | mybinder |
| Tues, 2/25 | Workshops 7 and 8 | |
| Wed, 2/26 | Midterm Review | |
| Thurs, 2/27 | Midterm Exam | |
| Thurs, 2/27 | Homework 3 Due | |
| Mon, 3/3 - Fri, 3/7 | Spring Break | |
| Tues, 3/11 | Lecture 14: Kernel Methods and SVMs | pptx | pdf |
| Wed, 3/12 | Workshop 9: PyTorch | materials |
| Thurs, 3/13 | Lecture 15: Randomized SVD | pptx | pdf |
| Thurs, 3/13 | Final Project Proposals Due | |
| Sat, 3/15 | Homework 4 Released | pdf | handout |
| Tues, 3/18 | Lecture 16: Biological Algorithms I: Optimization | pptx | pdf |
| Wed, 3/19 | Workshop 10: PowerBI | |
| Thurs, 3/20 | Lecture 17: Biological Algorithms II: Neural Networks | pptx | pdf |
| Tues, 3/25 | Lecture 18: Backpropagation | pptx | pdf |
| Wed, 3/26 | Workshop 11: scikit-learn | Thurs, 3/27 | Lecture 19: Convolutional Neural Networks | pptx | pdf |
| Thurs, 3/27 | Final Project Update #1 Due | |
| Mon, 3/31 | Homework 4 Due; Homework 5 Released | pdf | handout |
| Tues, 4/1 | Lecture 20: Recurrent Neural Networks | pptx | pdf |
| Wed, 4/2 | Workshop 12: OpenCV | |
| Thurs, 4/3 | Lecture 21: Deep Autoencoders | pptx | pdf |
| Tues, 4/8 | ||
| Wed, 4/9 | Workshop 13: Azure ML | |
| Thurs, 4/10 | Lecture 22: Transformers | pptx | pdf |
| Thurs, 4/10 | Final Project Update #2 Due | |
| Tues, 4/15 | Lecture 23: Generative AI | pptx | pdf |
| Tues, 4/15 | Homework 5 Due | |
| Wed, 4/16 | Workshop 14 | |
| Thurs, 4/17 | Lecture 24 | |
| Tues, 4/22 | Final Project Presentations | |
| Wed, 4/23 | Final Project Presentations | |
| Thurs, 4/24 | Final Project Presentations |