Machine Learning at Brown University
Welcome to CSCI1420!
How can artificial systems learn from examples and discover information buried in data? We explore the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised learning. Specific topics include empirical risk minimization, probably approximately correct learning, kernel methods, neural networks, maximum likelihood estimation, the expectation maximization algorithm, and principal component analysis. This course also aims to expose students to relevant ethical and societal considerations related to machine learning that may arise in practice.
Time: 2:30 - 3:50pm, Tuesday & Thursday
Location: MacMillan Hall 115
Waitlist Info
Please send an override request through CAB. No further action is necessary. Whenever slots are available, codes are sent out in the evening of every workday. Starting during the shopping period, codes that are unredeemed for 24 hours will be revoked.
Inquiries regarding “position on the waitlist” and/or “likelihood of joining the course” will not be addressed.
Schedule
Lecture slides will be posted on EdStem after each lecture.
| Date | Topics | Chapters | Notes | Code |
|---|---|---|---|---|
| Thu, Sep 4 | Intro, ERM framework | 1, 2.0, 2.1, 2.2 | ||
| Tue, Sep 9 | Halfspaces and Perceptron | 9.0, 9.1.0, 9.1.2 | ||
| Thu, Sep 11 | Linear and Polynomial Regression | 9.2 | ||
| Tue, Sep 16 | Logistic Regression | 9.3, 12.1.1, 14.0, 14.1.0 | End of shopping period | |
| Thu, Sep 18 | SGD, Data Prep, and other Practicalities | 14.3.0, 14.5.1 | ||
| Tue, Sep 23 | PAC Learning | 2.3, 3 | ||
| Thu, Sep 25 | The Bias-Complexity Tradeoff | 5 | ||
| Tue, Sep 30 | Model Selection, Validation, and Regularization | 11.0, 11.2, 11.3, 13.1, 13.4 | Change grading option deadline | |
| Thu, Oct 2 | Boosting | 10 | ||
| Tue, Oct 7 | Decision Trees | 18 | ||
| Thu, Oct 9 | Learning via Uniform Convergence | 4 | ||
| Tue, Oct 14 | VC Dimension | 6, 9.1.3 | ||
| Thu, Oct 16 | Naive Bayes | 24.0, 24.1, 24.2 | ||
| Tue, Oct 21 | K-Nearest Neighbors / Fairness in Machine Learning | 19 | ||
| Thu, Oct 23 | Support Vector Machines | 15 | ||
| Tue, Oct 28 | Kernel Methods | 16 | ||
| Thu, Oct 30 | Neural Networks | 20.0, 20.1, 20.2, 20.3 | ||
| Tue, Nov 4 | Backpropagation | 20.6 | ||
| Thu, Nov 6 | Deep Learning | |||
| Tue, Nov 11 | K-Means | 22.0, 22.2, 22.5 | ||
| Thu, Nov 13 | Expectation Maximization | 24.4 | ||
| Tue, Nov 18 | Principal Component Analysis | 23.0, 23.1 | ||
| Thu, Nov 20 | Cutting Edge Machine Learning | |||
| Tue, Nov 25 | Thanksgiving Break - No Lecture | No hours or EdStem monitoring | ||
| Thu, Nov 27 | Thanksgiving Break - No Lecture | No hours or EdStem monitoring | ||
| Tue, Dec 2 | Ethics in Machine Learning | |||
| Thu, Dec 4 | TBD |
Homework Policy
All assignments are due at 12:00pm noon. Written and programming assignments are to be submitted to Gradescope. See the missive for more information on late days and extensions.
Assignments
The report template can be found here.
| Description | Release | Due | Latex | Solutions |
|---|---|---|---|---|
| #1. Review, Python | Sep 4 | Sep 11 | Latex | |
| #2. Halfspaces, Linear and Polynomial Regression | Sep 11 | Sep 18 | Latex | |
| #3. Logistic Regression | Sep 18 | Sep 25 | Latex | |
| #4. PAC Learning and the Bias-Complexity Tradeoff | Sep 25 | Oct 2 | Latex | |
| #5. Model Selection, Validation, and Regularization | Oct 2 | Oct 9 | Latex | |
| #6. Boosting and Decision Trees | Oct 9 | Oct 16 | Latex | |
#7. Uniform Convergence and VC Dimension | Oct 16 | Oct 23 | ||
#8. Naive Bayes and Fairness | Oct 23 | Oct 30 | ||
#9. SVM and Kernels | Oct 30 | Nov 6 | ||
#10. Neural Networks | Nov 6 | Nov 13 | ||
#11. Deep Learning | Nov 13 | Nov 20 | ||
#12. Clustering | Nov 20 | Dec 2 | ||
Final Exam | Dec 14 | Dec 16 |
Calendar
Refer to the calendar below for the most up-to-date lecture and office hour schedule.
Meet the team
Lorenzo De Stefani
he/him | Professor
Favorite Sport: Golf
Jaideep Naik
he/him | HTA
Favorite Sport: Soccer
Hi, I'm a senior from CT studying APMA-CS. Big fan of travelling, playing soccer, and working out. And my dog. And new Beli spots.
Muhiim Ali
she/her | GTA
Favorite Sport: Soccer
Fifth Year Masters student in computer science. These emojis describe well: 🌮💻🤸🏿♀️🌮
Andrew Gao
he/him | UTA
Favorite Sport: Basketball
Hi, I'm a senior from Cupertino, CA studying APMA-CS. In my free time, I like playing the violin, watching the Warriors, and playing Brawl Stars. Looking forward to TAing this semester!
Arjan Chakravarthy
he/him | UTA
Favorite Sport: Badminton
Hi, I'm Arjan, and I'm a senior studying CS. In my free time, I enjoy playing badminton and trying new food. Excited to be your TA this semester!
Eric Kim
he/him | UTA
Favorite Sport: Soccer
Hi! I'm a senior studying CS-Econ. In my free time, I like watching/playing soccer, working out, and watching cooking videos. Excited to working with you all this semester!
Peter Popescu
he/him | UTA
Favorite Sport: Climbing
Heya! I'm Peter, a senior studying cs and apma. I love climbing, playing games with friends, and messing with my mac's dotfiles. Ask me anything about stationery or jazz fusion!
Sammy Liu
he/him | UTA
Favorite Sport: Tennis
Hey and welcome to ML! I'm a junior studying CS and APMA. Outside of class, I love going to the gym, learning to make coffee, and playing spikeball on the Main Green. Looking forward to a great semester!
Sam Bradley
he/him | UTA
Favorite Sport: Football
Hey! My name is Sam and I am a junior studying APMA-CS. In my free time, I enjoy running, biking, backpacking, and anything else outdoors. I also love photography - @coolpeoplewarmcolors!