1.2 Machine Learning AI Models - AAIA Domain 1 - Part A - AI Models, Considerations & Requirements

Описание: 00:00 Session Overview & Objectives
Machine Learning (ML).
Neural Networks and Deep Learning.

00:03 The AI Technology Hierarchy
The "Nesting Doll" Framework:
Doll 1 (Largest): Artificial Intelligence
Doll 2: Machine Learning (ML) – resides inside AI.
Doll 3: Neural Networks (NN) – resides inside ML.
Doll 4 (Smallest): Deep Learning (DL) – extends from NN.

00:26 Defining Machine Learning (ML)
Definition: An innovative subfield of AI using models to learn from data without explicit programming.
The Engine: Built on advanced mathematical concepts.
Auditor Note: You don't need to be a mathematician, but you must recognize ML leverages these statistical methods.

00:54 The Driver: Big Data & Evolution
The Catalyst: Massive data generation via Social Media, IoT, and Lidar.
The Mechanism: ML algorithms "eat" data to heighten predictive accuracy.
Evolution: Similar to human practice—performance improves with experience.
Result: Exposure to real-world scenarios refines pattern identification and prediction.

01:26 The Auditor's Perspective
Objective: Understand the inner workings of algorithms.
Value: Enables identification of system limitations and potential biases.
Analogy: You must understand how the engine works to effectively inspect the car.

02:10 ML Paradigms & The Labeling Challenge
Three Main Paradigms: Supervised, Unsupervised, and Reinforcement Learning.
Key Concept: Labels.

02:23 Labeling Challenges:
Volume: Manual labeling is expensive and slow.
Ambiguity: Subjective categories require expert input.
No Ground Truth: Correct answers may be unknown in new domains.

03:15 Paradigm 1: Supervised Learning
Analogy: A student learning with a teacher's answer key.
Method: Uses labeled data to train models.
Process: Maps input data to output labels by learning correlations.
Outcome: The machine predicts outcomes for new, unknown data based on training.

03:45 Supervised Learning Classifications
Type 1: Regression
Predicts a continuous, quantitative outcome (Result = Number).
Type 2: Classification
Identifies a category or qualitative result (Result = Bucket).

04:20 Risks: Supervised Learning
Dependency: Heavily relies on human input and data integrity.
Critical Risks:
Data quality issues.
Inherited bias.
The Trap: If the training data is biased, the model learns and replicates that bias.

04:33 The Hybrid: Semi-Supervised Learning
Definition: The middle ground utilizing small labeled datasets + large unlabeled datasets.
Use Case: Deployed when manual labeling is too expensive or tedious.
Function: Uses labeled data to formulate assumptions about the unlabeled data.

04:55 Paradigm 2: Unsupervised Learning
Analogy: A detective working alone.
Input: Raw, unlabeled data (No human guidance/answer key).
Goal: Detect underlying patterns, relationships, and data structures.

05:16 Unsupervised Learning Techniques
1. Clustering Methods:
2. Association Rules:
3. Dimensionality Reduction:

06:15 Risks: Unsupervised Learning
Primary Risk: Lack of explainability.
"Black Box" issue
Mitigation:*Human in the Loop (HITL)

06:35 Paradigm 3: Reinforcement Learning (RL)
Focus: Decision making and motor control.
Objective: Train an intelligent agent to maximize cumulative reward.
Environment: Complex and dynamic.
Learning Style: Autonomous learning derived from consequences.

06:51 RL Analogies & Safety Protocols
Analogy: Training a dog.
Positive Reward (Treat) = Moving closer to goal.
Negative Reward (No Treat) = Moving away.
Risk: High autonomy during the exploration phase.
Control: Leverage Human in the Loop principles to prevent harmful actions while the model learns.

07:27 Introduction to Neural Networks (NN)
Design: Biomimicry of the human brain.
Function: Identifies patterns and sifts through data.
Process: Adapts and refines understanding with every iteration.

07:41 Neural Network Architecture
Structure: Think of an assembly line.
Layer 1: Input Layer – Receives data.
Layer 2: Hidden Layers – The "Magic" layer where computation and learning occur.
Note: More layers = Ability to learn more complex patterns.
Layer 3: Output Layer – Produces the final prediction.

08:04 Key Neural Network Types
Feedforward (FNN):
Uni-directional flow (Input to Output). Used for regression/classification.
Recurrent (RNN):
Designed for sequential/time-series data. Loops information. Used for translation/speech.
Convolutional (CNN):
Specializes in grid-like data. Used for Computer Vision/Medical Imaging.

08:51 Deep Learning (DL) Explained
Foundation: Neural Networks are the building blocks.
Threshold: A network is "Deep Learning" if it has -3 layers (including input/output).
Advantage: Autonomous feature extraction.
Applications: High accuracy in NLP and Autonomous Systems.

09:17 Section 1.2 Summary
AI Hierarchy (AI - ML - NN - DL).
Three Machine Learning Paradigms.
Specific Neural Network Architectures.

09:28 Action Item: Visit Roocloud.com to complete Chapter MCQs.