2526-trendsinai

In deze github repository vind je het cursusmateriaal voor de cursus Trends In AI.

De inhoud kan aangepast worden naarmate de lessen vorderen.

Het cursusmateriaal is deels in het Nederlands en deels in het Engels.

• Les 1

  • les1_0_praktisch.md
  • les1_1_inleiding.md
  • les1_2_generative_ai_project_lifecycle.md
  • les1_3_scope_types_llms.md
  • notebooks/les1_4_praktisch.ipynb
  • les1_5_oefen.md

• Les 2

  • notebooks/les2_0_prompting.ipynb
  • notebooks/les2_1_prompt_engineering_openai.ipynb
  • notebooks/les2_2_prompt_engineering_llama.ipynb

• Les 3

  • les3_0_quantization.md
  • notebooks/les3_1_embeddings.ipynb
  • les3_2_vectorstores.md

  data: onder Documenten, op Chamilo

  referenties:

  • deeplearning.ai - Understanding and Applying Text Embeddings
  • deeplearning.ai - Vector Databases: from Embeddings to Applications

• Les 4

  • les3_2_vectorstores.md -> vervolg
  • notebooks/les3_1_navigable_small_world.ipynb
  • notebooks/les3_2_navigable_small_world_solution.ipynb
  • notebooks/les4_3_pinecone_semantic_search.ipynb

• Les 5

  • les5_0_LLM_powered_applications.md
  • notebooks/les5_1_langchain_RAG_project_exercise.ipynb

  data: onder Documenten, op Chamilo (lbdl.pdf)

  referenties:

  • coursera.org: Generative AI with Large Language Models

• Les 6

  • les6_0_finetuning_and_evaluation.md

  referenties:

  • coursera.org: Generative AI with Large Language Models

• Les 7

  • les7_0_prompt_injection.md
  • les7_1_adversarial_ai.md
  • les7_2_RLHF.md

  referenties:

  • deeplearning.ai - Red Teaming LLM Applications
  • (coursera.org - Introduction to Prompt Injection Vulnerabilities)

• Les 8

  • les8_0_bias.md
  • notebooks/les8_1_data_bias.ipynb
  • notebooks/les8_2_statistical_parity.ipynb
  • notebooks/les8_3_aif360_tutorial.ipynb
  • notebooks/les8_4_salary_prediction_classification.ipynb
  • notebooks/les8_4_salary_prediction_classification_reworked.ipynb

  data: download van kaggle (zie ipynb) onder Documenten, op Chamilo: german.data, h181.zip, h192.zip (ref. medical_expenditure)

• Les 9

  • les9_0_introduction.md
  • notebooks/les9_1_feature_importance_explainability_in_one_line.ipynb
  • notebooks/les9_2_global_interpretability.ipynb
  • notebooks/les9_3_local_interpretability.ipynb
  • notebooks/les9_4_treeSHAP_toy_example.ipynb

  referenties: Om deze les te maken, gebruikte ik:

  • https://www.bluecourses.com/ - Machine Learning Essentials - by Bart Baessens and Tim Verdonck.
  • @book{leborgne2022fraud, title={Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook}, author={Le Borgne, Yann-A{"e}l and Siblini, Wissam and Lebichot, Bertrand and Bontempi, Gianluca}, url={https://github.com/Fraud-Detection-Handbook/fraud-detection-handbook}, year={2022}, publisher={Universit{'e} Libre de Bruxelles} }

• Les 10

  • notebooks/les10_0_shapley.md
  • notebooks/les10_1_lime.md
  • notebooks/les10_0_saliency_mappings.md

• Les 11

  • les11_0_AI_act.md

• Les 12

  • les12_0_ethiek_in_AI.md
  • les12_1_ethiek_in_AI.pptx
  • les12_2_AI_for_researchers.md