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Session 1: Workflow and ML regression |
Interactive slides |
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R code |
Python code (View online) |
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Session 2: Classification |
Interactive slides |
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R code |
Python code (View online) |
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Session 3: Ensembling and Clustering |
Interactive slides |
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Example R code for clustering |
Python code (View online) |
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Session 4: Causal and non-causal ML |
Interactive slides |
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Python code (View online) |
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Session 5: Bias and Fairness |
Interactive slides |
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Python code (View online) |
Example R code for SHAP |
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Session 6: Text Analytics |
Interactive slides |
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Python code (View online) |
Example R code for text analytics |
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Session 7: Linguistics |
Interactive slides |
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Example R code for text analytics |
Python code (View online) |
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Implementing word2vec in Colab |
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Session 8: Topic modeling |
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Python code for LDA (View online) |
R code for STM and ETM (View online). This also includes code to build a word2vec model in R. |
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Session 9: Transformers |
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Python code for BERTopic (View online) |
BERTopic visualizations: base topics, base heatmap, base time, base class, custom topics, custom heatmap |
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Python code for FinBERT usage |
Pytorch code for FinBERT fine-tuning |
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Python code for FinBERT fine-tuning on new data (View online) |
Pytorch code for FinBERT inference of the fine-tuned model (View online) |
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Session 10: Understanding LLMs |
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Python code for OpenAI API (View online) Includes replications of 2 of the papers |
Building a tiny GPT from scratch |
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Session 11: Using LLMs |
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Session 12: Images as Data |
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Python code for CNNs and object detection (View online) (Colab version) |
Image and text embeddings with CLIP |
