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Class Description
Every technical product is now incorporating machine learning at an explosive rate. But most people, even those with strong technical skills, don't understand how it works, what its capabilities are, and what security risks come with it. In this workshop, we'll make machine learning models using simple Python scripts, train them, and evaluate their value. Projects include computer vision, breaking a CAPTCHA, deblurring images, regression, and classification tasks. We will perform poisoning and evasion attacks on machine learning systems, and implement deep neural rejection to block such attacks.
No experience with programming or machine learning is required, and the only software required is a Web browser. We will use TensorFlow on free Google Colab cloud systems.
Textbooks
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Required
Github
AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence Optional
Quizzes
The quizzes are multiple-choice, online, and open-book. However, you may not ask other people to help you during the quizzes. You will need to study the textbook chapter
before the lecture covering it, and take the quiz before that class.
Each quiz is due 30 min. before class. Each quiz has 5 questions, you have ten minutes to take it, and you can make two attempts. If you take the quiz twice, the higher score counts.
Don't use CCSF's Canvas system for this class. Instead, all students
should use this Canvas server:
Enroll Here ·
View Course ·
Reset password
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Schedule |
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| Date | Topic |
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| Mon, July 22 |
1 The Machine Learning Landscape
OWASP Top Ten
Projects: GL badges: Google Learning
ML 130: Prompt Injection
2 End-to-End Machine Learning Project
Project: ML 100: Machine Learning with TensorFlow
3 Classification
Project: ML 105: Classification |
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| Tue, July 23 |
4 Training Models
Projects: ML 101: Computer Vision
ML 102: Breaking a CAPTCHA
ML 103: Deblurring Images
5 Support Vector Machines
Project: ML 112: Support Vector Machines
6 Decision Trees
Project: ML 113: Decision Trees
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| Wed, July 24 |
7 Ensemble Learning and Random Forests
Project: ML 114: Ensemble Learning and Random Forests
8 Dimensionailty Reduction
Projects: ML 115: Dimensionality Reduction
ML 109: Poisoning Labels with SecML
9 Unsupervised Learning Techniques
Project: ML 116: k-Means Clustering |
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| Thu, July 25 |
10 Introduction to Artificial Neural Networks
Project: ML 107: Evasion Attack with SecML
11 Training Deep Neural Networks
Project: ML 108: Evasion Attack on MNIST dataset
12 Custom Models and Training with Tensorflow
Projects: ML 111: Poisoning the MNIST dataset
ML 140: Deep Neural Rejection
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| Fri, July 26 |
13 Loading and Preprocessing Data with Tensorflow
Projects: ML 120: Bloom LLM
ML 121: Prompt Engineering Concepts
ML 122: Comparing LLMs on Colab (10 pts + 10 extra)
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Lectures
The Fundamentals of Machine Learning
1 The Machine Learning Landscape
KEY ·
PDF
OWASP Top 10 Machine Learning Security Risks ·
KEY ·
PDF
OWASP Top 10 for LLM (PDF)
2 End-to-End Machine Learning Project
KEY ·
PDF
3 Classification
KEY ·
PDF
4 Training Models
KEY ·
PDF
5 Support Vector Machines
KEY ·
PDF
6 Decision Trees
KEY ·
PDF
7 Ensemble Learning and Random Forests
KEY ·
PDF
8 Dimensionailty Reduction
KEY ·
PDF
9 Unsupervised Learning Techniques
KEY ·
PDF
Neural Networks and Deep Learning
10 Introduction to Artificial Neural Networks
KEY ·
PDF
11 Training Deep Neural Networks
KEY ·
PDF
12 Custom Models and Training with Tensorflow
13 Loading and Preprocessing Data with Tensorflow
14 Deep Computer Vision Using Convolutional Neural Networks
15 Processing Sequences Using RNNs and CNNs
16 Natural Language Processing with RNNs and Attention
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