Machine Learning Topics

NumPy Fundamentals

• Understanding Axis(Dim) Operations

Clustering Algorithms

• K-Means Clustering

Deep Learning Fundamentals

• The XOR Problem: Nonlinearity in Deep Learning
• Likelihood-Based Loss Functions
• Hidden Units and Activation Functions
• Architecture Design: Depth vs Width
• Back-Propagation and Other Differentiation Algorithms
• Chapter 7.11: Bagging and Other Ensemble Methods
• Chapter 7.12: Dropout
• Chapter 7.13: Adversarial Training
• Chapter 7.14: Tangent Distance, Tangent Prop and Manifold Tangent Classifier
• Chapter 8.1: How Learning Differs from Pure Optimization

Deep Learning in the Life Sciences

• Overview
• What a Genomic Model Learns Depends on Its Target

Stanford CS336: Language Modeling from Scratch

• Overview
• A Tokenizer Changes the Work a Language Model Has to Do

Advanced Large Language Model Agents

• Overview
• Lecture 10: Advanced Topics in Theorem Proving
• Multimodal Autonomous AI Agents
• Coding Agents and AI for Vulnerability Detection
• Inference-Time Techniques for LLM Reasoning

CMU 10-414/714: Deep Learning Systems

• Overview
• Lecture 8: Normalization, Dropout, and Model State
• Lecture 7: Neural Network Library Abstractions
• Lecture 6: Optimization and Initialization
• Lecture 3 Part II: Manual Neural Networks
• Lecture 3: Manual Neural Networks
• Lecture 2: ML Refresher / Softmax Regression

Papers in Deep Learning

• Overview
• LoRA: Low-Rank Adaptation of Large Language Models

JAX

• Overview
• Scaling Up
• JAX NumPy
• Introducing Flax NNX
• JAX AI Stack

Natural Language Processing

• Overview
• CMU Advanced NLP Lecture 21: Mixture of Experts
• CMU Advanced NLP Lecture 16: Reinforcement Learning for LLMs
• CMU Advanced NLP Lecture 9: Decoding Algorithms
• CMU Advanced NLP Lecture 2: Learned Representation
• CMU Advanced NLP Lecture 1: Introduction and Fundamentals

Reinforcement Learning

• Overview
• David Silver RL Course - Lecture 9: Exploration and Exploitation
• David Silver RL Course - Lecture 8: Integrating Learning and Planning
• David Silver RL Course - Lecture 7: Policy Gradient Methods
• David Silver RL Course - Lecture 6: Value Function Approximation
• David Silver RL Course - Lecture 1: Introduction to Reinforcement Learning

ML HW-SW Codesign

• Overview
• Efficient AI Lecture 22: Quantum Machine Learning I
• Efficient AI Lecture 21: On-Device Training and Transfer Learning
• Efficient AI Lecture 20: Distributed Training (Part 2)
• Efficient AI Lecture 19: Distributed Training (Part 1)
• Efficient AI Lecture 18: Diffusion Models
• Efficient AI Lecture 17: Efficient GANs, Video, and Point Cloud
• Efficient AI Lecture 16: Vision Transformer
• Efficient AI Lecture 15: Long-Context LLM
• Efficient AI Lecture 14: LLM Post-Training
• Efficient AI Lecture 13: LLM Deployment Techniques
• Efficient AI Lecture 12: Transformer and LLM
• Efficient AI Lecture 11: TinyEngine
• Efficient AI Lecture 10: MCUNet and TinyML
• Efficient AI Lecture 9: Knowledge Distillation
• Efficient AI Lecture 8: Neural Architecture Search (Part II)
• Efficient AI Lecture 1: Introduction

Classification Algorithms

• Logistic Regression

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