The goal of this course is to provide a broad introduction to the key ideas in machine learning. The emphasis will be on intuition and practical examples rather than theoretical results. Through a variety of lecture examples and programming projects, you will learn how to apply powerful machine-learning techniques to new problems, how to run evaluations and interpret results, and how to think about scaling up from thousands of data points to billions.
This class meets for one 90 min class periods each week. It includes four guided programming projects and one more open-ended final project.
All materials in this course are posted on GitHub in the form of Jupyter notebooks.
Course Prerequisites
Core data science courses: research design, storing and retrieving data, exploring and analyzing data.
Undergraduate-level probability and statistics. Linear algebra is recommended.
Programming Prerequisites
Python (v3). We will be primarly using numpy and scikit-learn.
Jupiter and JupiterLab notebooks. You can install them in your computer using pip or Anaconda. More information here.
Git(Hub), including clone/commmit/push from the command line. You can sign up for an account here.
OS
Mac/Windows/Linux are all acceptable to use.
Textbook
Assignments
Final Project
| Day | Lecture | Lecture Materials | Deadlines (Sunday of the week, 11:59 pm PT) |
|---|---|---|---|
| Supervised Learning | |||
| 01/06 | Introduction | Week 1 | |
| 01/13 | Nearest Neighbors | Week 2 | |
| 01/20 | Naive Bayes | Week 3 | Assignment 1 |
| 01/27 | Decission trees | Week 4 | |
| 02/03 | Cross-validation and Ensemble learning | Week 5 | Assignment 2 |
| 02/10 | Regression analysis | Week 6 | Final project: group and dataset |
| 02/17 | Neural networks | Week 7 | |
| 02/24 | Support vector machines | Week 8 | |
| Unsupervised Learning | |||
| 03/03 | Cluster analysis | Week 9 | Assignment 3 |
| 03/10 | Gaussian mixture models | Week 10 | |
| 03/17 | Dimensionality reduction | Week 11 | Final project: baseline presentation (during class time) |
| 03/21 | [Spring Break] | - | |
| Other Topics | |||
| 03/31 | Network analysis | Week 12 |
Assignment 4 |
| 04/07 | Recommender systems | Week 13 | |
| 04/14 | Wrap-up | Week 14 | Final project: final presentation (during class time) |
For the final project you will form a group (3-4 people are ideal; 2-5 people are allowed; no 1 person group allowed). Grades will be calibrated by group size. Your group can only include members from the section in which you are enrolled.
Do not just re-run an existing code repository; at the minimum, you must demonstrate the ability to perform thoughtful data preprocessing and analysis (e.g., data cleaning, model training, hyperparameter selection, model evaluation).
The topic of your project is totally flexible (see also below some project ideas).
Deadlines to remember:
A few project ideas:
Baseline presentation. Your slides should include:
Final presentation. Your slides should include:
| Participation | 5% |
| Programming Assignments | 15% (x4) |
| Final project | 35% |
Integrating a diverse set of experiences is important for a more comprehensive understanding of machine learning. I will make an effort to read papers and hear from a diverse group of practitioners, still, limits exist on this diversity in the field of machine learning. I acknowledge that it is possible that there may be both overt and covert biases in the material due to the lens with which it was created. I would like to nurture a learning environment that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion, ability, veteran status, etc.) in the spirit of the UC Berkeley Principles of Community.
To help accomplish this, please contact me or submit anonymous feedback through I School channels if you have any suggestions to improve the quality of the course. If you have a name and/or set of pronouns that you prefer I use, please let me know. If something was said in class (by anyone) or you experience anything that makes you feel uncomfortable, please talk to me about it. If you feel like your performance in the class is being impacted by experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you. Also, anonymous feedback is always an option, and may lead to me to make a general announcement to the class, if necessary, to address your concerns.
As a participant in teamwork and course discussions, you should also strive to honor the diversity of your classmates.
If you prefer to speak with someone outside of the course, MICS Academic Director Lisa Ho, I School Assistant Dean of Academic Programs Catherine Cronquist Browning, and the UC Berkeley Office for Graduate Diversity are excellent resources. Also see the following link.