UofU Data Science

(Q)LoRA; Course wrap

Quantization & KV cache

DPO & Reasoning LMs

SFT data & RLHF

LLM-as-judge; Instruction finetuning / SFT

Summarization & Text generation evaluation

Answer extraction & RAG

QA landscape & Retrieval

Review & Pretraining data

Pretraining & finetuning

Transformers Part 2

Transformers Part 1

Subword tokenization

Attention

seq2seq (through the lens of NMT)

Language modeling

Word embeddings: Eval & analysis + Course preview

Word embeddings: word2vec skip-gram

Neural networks foundations: Part 2

Neural networks foundations: Part 1

Machine learning foundations: Part 2

Machine learning foundations: Part 1

Course Overview & Logistics

Lecture 27: Practical advice for using machine learning

Lecture 26: Neural networks (continued)

Lecture 25: Neural networks (continued)

Lecture 24b: Neural networks

Lecture 24a: Loss minimization (revisited)

Lecture 23b: Logistic regression

Lecture 23a: Bayesian learning (continued)