Welcome to the AIBridge Course homepage.
The purpose of AIBridge is to bridge the gap between computer science and other disciplines. To many, working with AI might seem like an unreachable objective. However, in reality, one week is enough to get started. AIBridge will provide basic programming capability in Python and knowledge of object-oriented programming as well as the concepts behind machine learning and how to implement it using a popular toolbox, Scikit-Learn. Students work to complete a personally-defined project using techniques in AI, with data from their own research or with problems supplied by the Course. This one week course will be hosted in-person at UC Davis and will target mainly undergraduate and non-technical graduate students.
The course is taught by Prof. Xin Liu in collaboration with Houjun “Jack” Liu, Samuel Ren, and Albara Ah Ramli.
Evergreen Resources
- Python Tutorial: W3 Schools
- Python Documentation: Python.org
- SciKit Documentation: scikit-learn.org
- Iris Dataset: UCI DB, or, for better user experience, scikit
- Wine Dataset: UCI DB
- Class Discord: Invite
- Data-Loading Cheat-Sheet: Colab
When in doubt…
- Google it! Try it!
- Andrew Ng’s Machine Learning Suite of Courses
DONE Day 1: Python Basics
On Monday, 06/27/2022, we covered the basics of Python so that we are all up to speed to perform basic ML with the Scikit Learn toolkit.
- Introductory Remarks: Slides
- Lecture on Python Basics: Slides
- Lab Exercises: Morning Lab Notes, Afternoon Lab Notes
- Colab Notebooks: Morning Lecture Notebook, Morning Lab Notebook, Afternoon Lecture Notebook, Afternoon Lab Notebook
Day 1 feedback survey: Link
DONE Day 2: OOP + Linear Models
Today, we are going to cover the basic intuition and terminology behind Object Oriented Programming, as well as introduce two simple, linear approaches to Machine Learning tasks: linear regression and logistic regression.
- Lecture on OOP and more on functions (morning): Slides
- Lecture on Linear and Logistic Regression (afternoon): Slides
- Lab Exercises: Morning Lab Notes, Afternoon Lab Notes
- Colab Notebooks: Morning Lecture Notebook, Morning Lab Notebook, Afternoon Lab Notebook
Day 2 feedback survey: Link
DONE Day 3: Data + Classifier
Today, we are going to cover data cleaning, and three more classifiers!
- Lecture on data cleaning and pandas (morning): Slides
- Lecture on three classification algorithms (afternoon): Slides
- Lab Exercises: Morning Lab Notes, Afternoon Lab Notes
- Colab Notebooks: Morning Lab Notebook, Afternoon Lab Notebook
Day 3 feedback survey: Link
DONE Day 4: Operations and Clustering
Today, we are going to work on the validation operations tools, and talk about clustering
- Lecture on training and data operations (morning): Slides
- Lecture on clustering and feature operations (afternoon): Slides
- Lab Exercises: Morning Lab Notes, Afternoon Lab Notes
- Colab Notebooks: Afternoon Notebook
Day 4 feedback survey: Link
Day 5: Closing Thoughts
Today, we are going to tie some loose ends with missing data, error analysis, semi supervised learning, cross validation, and ethics.
- Closing thoughts lecture (morning): Slides
Final Project: AIBridge Final Project
Day 5/Bootcamp feedback survey: Link
Other Links and Resources
- Tools we use: AIBridge Packages and Tools
- Cleaned Wine Dataset (try cleaning it yourself before using!): Google Drive
- Iris Data with Temperature (don’t use without instructions, though!): Google Drive