By the UCLA Association for Computing Machinery (ACM) AI
This is a two-quarter course taught by UCLA students to introduce high-school students to artificial intelligence and its many applications. The number of weeks required to complete the course and start date are flexible.
Artificial intelligence (AI) and machine learning (ML) have become extremely prevalent in society, from powering the voice assistants on our smartphones to improving medical diagnoses. Regardless of what areas high schoolers plan to pursue upon graduation, they’re likely to encounter AI and ML-related technologies in their future careers.
ACM AI is a student organization at UCLA dedicated to educating curious individuals about artificial intelligence and its applications. For 4 years, we’ve been hosting AI/ML workshops on campus for UCLA students, preparing them to pursue advanced opportunities in the fields. In the coming academic year, we hope to spread knowledge of AI/ML to high schoolers who are interested in exploring technology-related fields.
A list of topics we would like to cover is included below. Linked below each topic and its respective bullet points are resources to help those interested learn more about these individual topics. Please email ucla acm ai at gmail dot com with any requests, questions, or concerns.
- What is AI?
- Overview of artificial intelligence, its history, applications, and prevalence in society
- Our Fall 2019 presentation from this session
- Intro to artificial intelligence
- MIT Technology Review's Artifical Intelligence Section
- MIT news on AI
- NY Times news on AI
- What is ML?
- How is machine learning used to create artificially intelligent systems?
- Overview of key techniques: linear and logistic regression, neural networks, CNN algorithms, etc.
- Our Fall 2019 presentation from this session
- Intro to machine learning
- Good intro to various types of machine learning
- Python + loading datasets + plotting (with matplotlib)
- Will cover basics of Python programming
- Will discuss how data are stored and can be loaded
- Will explore different ways of visualizing datasets
- Our Fall 2019 presentation from this session
- Learn Python online in an interactive environment
- Really quick and dirty intro to Python (better as a refresher as opposed to first introduction)
- Scikit-learn tutorials
- Intro to scikit-learn ft pandas
- Linear regression
- Matrices and vectors
- Features and training examples
- Gradient descent
- Training vs. testing sets
- Why do we use gradient descent? (HIGHLY mathematical explanation, but a good starting point if you're interested in learning more theory / background)
- Logistic regression
- Recap: classification vs. regression
- How to transform linear regression into logistic regression using the sigmoid activation function
- Bayes’ theorem
- Normal distribution + anomaly detection
- Shallow neural networks
- Deep fully-connected neural networks
- Convolutional neural networks
- YOLO (object detection in images)
- Landmark detection (identifying facial features)