Machine learning is an exciting field of computer science that impacts many applications both from a consumer standpoint (e.g., Microsoft Kinect, iPhone’s Siri, Netflix recommendations) and the sciences and medicines (e.g., predicting genome-protein interactions, detecting tumors, personalized medicine). In this course, students will learng the fundamental theory and algorithms of machine learning and how to apply machine learning to solve problems. Prerequisites: A course in linear algebra and previous exposure to statistics and probability theory. Homeworks and project will require programming ability in Python, Matlab, R, or C/C++..
- Piazza: All annoucements, assignment clarifications, and slide corrections will be posted here. Make sure you check it on a regular basis.
- Office Hours: (Caveat - Office hours may change from time to time, in which case an announcement will be made on Piazza.)
- Joyce Ho: M 1:30 PM - 3:30 PM, W 9:30 AM - 12:00 PM @ MSC W414
- Rongmei Lin: F 12:00 PM - 2:00 PM @MSC N410
- Required: The Elements of Statistical Learning: Data Mining, Inference, and Prediction), by Trevor Hastie, Robert Tibshirani & Jerome Friedman
- Supplemental: Machine Learning: a Probabilistic Perspective, by Kevin Murphy
- Supplemental: Pattern Recognition and Machine Learning, by Christopher Bishop
|Introduction and Review|
|1||1/10||Overview & Course Logistics||
|2||1/12||Random Variables & Probability Review||
|3||1/17||Linear Algebra & Optimization Review||
|Supervised Learning I|
|4||1/19||Statistical Decision Theory & Linear Regression||
Lectures 4 - 5
Teams.csv (data file)
|Validation, Model Selection, and Theory|
|8||2/2||Learning Theory||Lecture 8|
|10||2/9||Bootstrap & Model Selection||Lectures 10-11||</td>|
|Supervised Learning II|
|12||2/16||Boosting, Trees & Additive Models||
|14||2/23||Ensembles & Random Forests||Lecture 14||
|15||2/28||Support Vector Machines||
|Supervised Learning II|
Neural Network Playground
Handwritten Digit Visualization
plot_mnist_filters.ipynb by Scikit-learn
|19||3/21||K Nearest Neighbors||Lecture 19||
|22||3/30||Clustering & Mixture Models||Lecture 22|
|23||4/4||Topic Models||Lecture 23||
|24||4/6||Hidden Markov Models||Lecture 24|
|25||4/11||Deep Learning||Lecture 25|
|26||4/13||Recommendation Systems||Lecture 26|
Assignment and Exam Policy
- Assignments are due electronically at 11:59 PM on Canvas.
- Each student receives six late days that can be used on assignments throughout the semester. These late days extend the deadline for 24 hours, and can be distributed amongst the 4-5 assignments, but no more than 3 late days may be used on any assignment.
- After the six late days are used, the assignment will be penalized 10% off per day up to 3 late days.
- Late days apply to the entire assignment, so handing in one problem late counts as a late day towards the whole assignment.
- After 3 late days on any given assignment you will receive no credit for the asignment. Exam
- All exams must be taken promptly at the required time.
- Requests for rescheduling the midterm exam will only be considered if the request is made at least a week prior to the exam date.
The above policies will be waived only in an “emergency” situation with appropriate documentation.
All class work is governed by the College Honor Code and Departmental Policy. It is acceptable and encouraged to discuss homeworks with other students. However, this should be noted on your submitted homework and all code and writeup must be written by yourself. Any code and writeup that is found to be similar is grounds for an honor code investigation by the Director of Gradute Studies, Laney Graduate School, and the honor council.