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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 on this Canvas server:
Enroll Here ·
View Course ·
Reset password
You can download the whole Canvas course
here
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Schedule |
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| Mon, July 22 |
1 The Machine Learning Landscape
OWASP Top Ten
Projects: GL badges: Google Learning
ML 130: Prompt Injection
Projects: ML 101: Computer Vision
ML 102: Breaking a CAPTCHA
ML 103: Deblurring Images
2 End-to-End Machine Learning Project
Project: ML 104: Analyzing Input Data
Projects: Google Learning
Security Risks
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| Tue, July 23 |
3 Classification
Project: ML 105: Classification
4 Training Models
5 Support Vector Machines
Project: ML 112: Support Vector Machines
6 Decision Trees
Project: ML 113: Decision Trees
ML 106: Data Poisoning
ML 107: Evasion Attack with SecML
ML 108: Evasion Attack on MNIST dataset
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| Wed, July 24 |
7 Ensemble Learning and Random Forests
Project: ML 114: Ensemble Learning and Random Forests
8 Dimensionality Reduction
Projects: ML 115: Dimensionality Reduction
9 Unsupervised Learning Techniques
Project: ML 116: k-Means Clustering
ML 123: Running Llama 3 Locally
ML 126: Building RAGs
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| Thu, July 25 |
10 Introduction to Artificial Neural Networks
ML 160: GitHub Copilot
ML 131: Generating Python Code with Bard
Violent Python Challenges
11 Training Deep Neural Networks
ML 110: Poisoning by Gradients
ML 111: Poisoning the MNIST dataset
12 Custom Models and Training with Tensorflow
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| Fri, July 26 |
ML 140: Deep Neural Rejection
ML 127: Encoding Text with BERT
ML 128: Using AnythingLLM to Embed Custom Data
13 Loading and Preprocessing Data with Tensorflow
ML 120: Bloom LLM
ML 121: Prompt Engineering Concepts
ML 122: Comparing LLMs on Colab
ML 129: Embedding Words with BERT
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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
KEY ·
PDF
13 Loading and Preprocessing Data with Tensorflow
KEY ·
PDF
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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