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README.md

IIIS-preliminary

Description

This is a summary (cheatsheet) for preliminary exam of IIIS, THU. Areas of the exam are mainly undergraduate level algorithm, AI and machine learning (subarea).

Scope of the Exam

General requirements (60%):

  1. Algorithms (Please refer to the requirement for the Theory Qualify exam, Algorithm 1- 12)
  2. Solving problem by search (AI Book Chapter 3)
  3. Beyond Classic search (AI Book Chapter 4)
  4. Basic Machine Learning (https://www.coursera.org/learn/machine-learning)
  5. Basic Deep Learning (http://cs231n.stanford.edu/syllabus.html, lecture 1-10)

Sub-area #1 (40%): Machine Learning and Deep learning

The rest of http://cs231n.stanford.edu/syllabus.html

Other things you should know:

  1. PCA (FML Ch 12.1)
  2. Random Forest (ESL Ch 15)
  3. Adaboost (FML Ch6)
  4. Gradient Boosting / Additive Models (ESL Ch 9 and 10)
  5. Clustering and unsupervised learning (ESL Ch 14.3)
  6. Bias-Variance Decomposition, Cross-validation (ESL Ch 7)
  7. PAC learning (FML Ch2)
  8. Online learning (FML Ch7)
  9. VC dimension (FML Ch3)
  10. Graphical models: Bayesian network and undirected graphical models (PRML Ch.8)
  11. Optional: Sampling and MCMC (PRML Ch 11)
  12. Optional: Reinforcement Learning (FML Ch14)
  13. Optional: Deep learning for NLP (http://web.stanford.edu/class/cs224n/)

Reference Book

  • [AI Book] Artificial Intelligence: a modern approach. Third Edition. Stuart Russell and Peter Norvig
  • [PRML] Pattern Recognition and Machine Learning, Bishop.
  • [FML] Foundations of Machine Learning. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
  • [ESL] The Elements of Statistical Learning Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, Jerome Friedman. 2008.