Stanford CS230 | Autumn 2025 | Lecture 2: Supervised, Self-Supervised, & Weakly Supervised Learning | Stanford Online

Kian Katanforoosh leads an interactive lecture structured in multiple parts, starting with a recap of the week's online learning, including neurons, layers, and deep neural networks. He then transitions into supervised learning projects like day and night classification, trigger word detection, and face verification, emphasizing industry-specific inputs and decision-making processes. The lecture progresses into self-supervised and weekly supervised learning, focusing on embeddings and their importance in AI systems. Katanforoosh also touches on adversarial attacks and defenses, and the session includes human experiments to refine data labeling strategies, architecture search, and the design of effective loss functions, with a strong emphasis on data collection and expert advice.

Outlines

Part 1: Introduction and Core Concepts

Part 2: Supervised Learning Projects - Classification & Detection

Part 3: Supervised Learning Projects - Face Verification & Identification

Part 4: Self-Supervised Learning and Multimodality

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Stanford CS230 | Autumn 2025 | Lecture 2: Supervised, Self-Supervised, & Weakly Supervised Learning

Stanford Online

Part 1: Introduction and Core Concepts

Introduction to CS230 and the Core Learning Setup

Variations in the Learning Setup and Neural Network Capacity

Understanding Neural Network Encoding and Recap of Key Concepts

Part 2: Supervised Learning Projects - Classification & Detection

Supervised Learning Project: Day and Night Classification

Data Input, Resolution, and Model Architecture for Day/Night Classification

Loss Function and Introduction to Trigger Word Detection

Data Distribution and Labeling for Trigger Word Detection

Labeling Strategy and Architecture Search for Trigger Word Detection

Part 3: Supervised Learning Projects - Face Verification & Identification

Introduction to Face Verification and Data Collection

Pixel Comparison and Encoding for Face Verification

Triplet Loss Function for Face Verification

Face Identification and K-Nearest Neighbors

Face Clustering and Introduction to Self-Supervised Learning

Part 4: Self-Supervised Learning and Multimodality

Contrastive Learning and Emergent Behaviors

Multimodality and Shared Embedding Spaces

Stanford CS230 | Autumn 2025 | Lecture 2: Supervised, Self-Supervised, & Weakly Supervised Learning

Stanford Online

Part 1: Introduction and Core Concepts

00:05Introduction to CS230 and the Core Learning Setup

Introduction to CS230 and the Core Learning Setup

06:15Variations in the Learning Setup and Neural Network Capacity

Variations in the Learning Setup and Neural Network Capacity

14:32Understanding Neural Network Encoding and Recap of Key Concepts

Understanding Neural Network Encoding and Recap of Key Concepts

Part 2: Supervised Learning Projects - Classification & Detection

18:52Supervised Learning Project: Day and Night Classification

Supervised Learning Project: Day and Night Classification

24:34Data Input, Resolution, and Model Architecture for Day/Night Classification

Data Input, Resolution, and Model Architecture for Day/Night Classification

31:16Loss Function and Introduction to Trigger Word Detection

Loss Function and Introduction to Trigger Word Detection

38:35Data Distribution and Labeling for Trigger Word Detection

Data Distribution and Labeling for Trigger Word Detection

45:46Labeling Strategy and Architecture Search for Trigger Word Detection

Labeling Strategy and Architecture Search for Trigger Word Detection

Part 3: Supervised Learning Projects - Face Verification & Identification

53:55Introduction to Face Verification and Data Collection

Introduction to Face Verification and Data Collection

1:02:07Pixel Comparison and Encoding for Face Verification

Pixel Comparison and Encoding for Face Verification

1:07:11Triplet Loss Function for Face Verification

Triplet Loss Function for Face Verification

1:12:26Face Identification and K-Nearest Neighbors

Face Identification and K-Nearest Neighbors

1:17:46Face Clustering and Introduction to Self-Supervised Learning

Face Clustering and Introduction to Self-Supervised Learning

Part 4: Self-Supervised Learning and Multimodality

1:25:34Contrastive Learning and Emergent Behaviors

Contrastive Learning and Emergent Behaviors

1:32:29Multimodality and Shared Embedding Spaces

Multimodality and Shared Embedding Spaces