Congrats, you found this page! This is my set of study questions, I try to test myself on subjects I want to remember with a interleaved and varied practice. Beware that some links may be broken.
When learning is hard, you’re doing important work.
Make It Stick
Researches show that re-reading, note taking, and cramming are not effective study techniques. What really makes things stick is being actively engaged with the content; therefore the list of questions.
Repeated effortful recall or practice helps integrate learning into mental models… When practice conditions are varied or retrieval is interleaved with the practice of other material, we increase our abilities of discrimination and induction and the versatility with which we can apply the learning in new settings at a later date.
Make It Stick
- Feature Selection
- Correlation-based Feature Subset Selection…
- Python Concurrency
- Machine Learning
- Statistics of Feature Selection
- Complex Systems
- What is the difference between feature selection and feature extraction?
- What is a greedy method?
- What is a heuristics?
- How the first best search algorithm works?
- What is a feature selection wrapper method?
- What is a feature selection filter method?
- What is the Pearson’s correlation equation?
- What is one of the test theory about good features from Ghiselli 1964?
- What the word foes mean?
- What the word unascertained mean?
- What the word tenet mean?
- True or False? I/O-Bound programs spend a lot of time doing computations
- Which type of concurrency is best for CPU-bound programs?
- If you have a CPU-bound program and you’re going to use the multiprocessing library, what’s a good number of processes to create?
- You have an I/O-bound program that takes about 2 seconds to run and that only gets run once a week. Which concurrency library should you use?
- What is the main difference between multiprocessing and (threading and asyncio)?
- What is concurrency?
- How threading works?
- What is pre-emptive multitasking?
- What is the specific use of pre-emptive multitasking for optimizing I/O and CPU bound process?
- How asyncio works?
- What is generalization?
- How to estimate the generalization ability?
- What is overfitting?
- What is unsupervised learning? What are the main techniques?
- What is supervised learning? What are the main techniques?
- What is a disjoint region?
- Which situations lead to overfitting?
- Who founded the theory of fuzzy logic?
- What are the components of a fuzzy system?
- What functions are used as fuzzy membership functions?
- What are the 4 components of fuzzy systems?
- What is induction?
- What is transduction?
- What is deduction?
- What is bagging?
- What is a parametric estimator?
- What is a nonparametric estimator?
- When to use parametric and nonparametric methods?
- What is probability?
- What is membership?
- What is the relationship of probability and membership?
- What is the relationship of these two with solving problems?
- What is the intuitive goal of the Bayes Theorem?
- How to calculate the probability of two events happen? P(AB)
- How to calculate the probability of one or other event happen? P(AvB)
- How marginalization works for Bayesian Learning?
- How MAP works for Bayesian Learning?
- What is the difference between exploration and exploitation?
- What is i.i.d?
- What is causality?
Statistics of Feature Selection
- What is the problem of feature selection for statistics?
- What is a null distribution?
- What are random probes?
- How to use the hypothesis test?
- What conclusions can be taken from the p-value?
- What shouold be assumed when performing a hypothesis test?
- When to use a wilcoxon test?
- How the wilcoxon test works?
- What are the possible hypothesis for univariate tests?
- What hyphotesis test and wilcoxon have in common?
- What are the two general methods categories can be used to feature subset selection?
- What are anytime algorithms?
- When randomization methods tend to be useful?
- What are the two type os randomization algorithms?
- Define both types of randomization algorithms?
- What is the advantange of randomization in comparison with other search methods?
- In which parts of feature selection randomization can be used?
- What is the trade off of using randomization?
- What to consider when constructing a randomized algorithm for feature selection? (Computational Methods of Feature Selection p.60)
- What characteristics the feature selection problem has that are critical to successful applications of randomization? (Computational Methods of Feature Selection p.59)
- What is a deterministic algorithm?
- What is a nondeterministic algorithm?
- What is the focus of the grey system theory?
- What is the scientific principle of simplicity?
- What is a grey number?
- What is Elixir match operator?
- In which scenarios Elixir shines?
- How to compare Elixir and Python?
- What are the properties that guarantee accuracy and availability for databases? (ACID)
- Why are Referential Integrity constraints important?
- How to set up a failover system?
- What are the downsides of MYSQL?
- What are the good practices of logging in databases?
- What is the difference between a database and a database management system?
- What are data-intensive applications
- Summarize the common needs of applycations into 5 general groups
- What is the difference between latency and response time?
- What is a percentile?