Lectures[ML Science]
15A: Meta Learning
AI-839
Preface
Welcome
Course
Lectures[ML Engg.]
01A: Grounding MLOps
02A: Grounding MLOps
02B: Discover
03A: Models for Modeling
03B: DevOps for ML
04A: Develop
04B: Monitor (Data)
05A: Monitor (Models)
05B: Deployment
06A: Evaluate
06B: Govern
Lectures[ML Science]
07A: Scaling Laws
07B: Sample Hardness
08A: Model Fitness
09A: Uncertainty Quantification
11B: Influence Functions
LLMs-Ops
13A: LLMs Introduction
13B: LLMs Ops
13C: Fullstack LLMs
Lectures[ML Science]
15A: Meta Learning
16A: Fairness & Bias
16B: Machine Unlearning
Homeworks
Projects
Tutorials
Talks
Notebooks
Sample Hardness
Sample Fitness
Conformal Prediction
ML Documentation
Project Card
Table of contents
Materials:
Pre-work:
In-Class
Post-Class
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Lectures[ML Science]
15A: Meta Learning
15A: Meta Learning
Materials:
Date: 19th, Nov, 20224, 11.30am IST
Pre-work:
In-Class
Post-Class
13C: Fullstack LLMs
16A: Fairness & Bias