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%):
- Algorithms (Please refer to the requirement for the Theory Qualify exam, Algorithm 1- 12)
- Solving problem by search (AI Book Chapter 3)
- Beyond Classic search (AI Book Chapter 4)
- Basic Machine Learning (https://www.coursera.org/learn/machine-learning)
- 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:
- PCA (FML Ch 12.1)
- Random Forest (ESL Ch 15)
- Adaboost (FML Ch6)
- Gradient Boosting / Additive Models (ESL Ch 9 and 10)
- Clustering and unsupervised learning (ESL Ch 14.3)
- Bias-Variance Decomposition, Cross-validation (ESL Ch 7)
- PAC learning (FML Ch2)
- Online learning (FML Ch7)
- VC dimension (FML Ch3)
- Graphical models: Bayesian network and undirected graphical models (PRML Ch.8)
- Optional: Sampling and MCMC (PRML Ch 11)
- Optional: Reinforcement Learning (FML Ch14)
- Optional: Deep learning for NLP (http://web.stanford.edu/class/cs224n/)
- [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.