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Decks in this class (29)
ST420 - Basic Notation and Concepts
What will capital letters denote 1,
For a random sample of a random v...,
How will we denote a multivariate...
18
cards
Chapter 1.1: Intro to Statistical Learning - Buzz words and basic concepts
What is statistics 1,
What is data science 2,
What are ai and ml what are some ...
11
cards
Chapter 1.2: Intro to Statistical Learning - First taste of Statistical Machine Learning
When estimating f what do you nee...,
What is the simple linear regress...,
What is the multiple linear regre...
15
cards
Chapter 1.3: Intro to Statistical Learning - Statistical learning theory - Loss, Risk and ERM
What is the formal notation for a...,
What are the following loss funct...,
What are the following loss funct...
25
cards
Chapter 1.4: Intro to Statistical Learning - Statistical learning theory - Excess Risk and the Bias-Variance Trade-off
What is the loss function and wha...,
What are some questions we have a...,
What is excess risk why is it alw...
24
cards
Chapter 2.1: Classification (Part I) - Classification concepts, LDA and QDA
What is the general setup of clas...,
What are the decision boundaries ...,
What are the discriminative appro...
14
cards
Chapter 2.2: Classification (Part I) - Logistic Regression
What is generative classification...,
What is a perceptron what is its ...,
How do we build on the perceptron...
13
cards
Chapter 2.3: Classification (Part I) - Neural Networks
What is the inspiration behind ne...,
How can perceptrons and binary lo...,
For perceptrons and binary logist...
20
cards
Chapter 3.1: Big Data - Issues arising from Big Data
How did the term big data come ab...,
What are the 4 big challenges ass...,
What is a genome how are theser r...
15
cards
Chapter 3.2: Big Data - Multiple testing
R how do you generally 101 variab...,
How to i analyse this data in r 2,
What is voodoo correlation and wh...
9
cards
Chapter 4.1: Regularisation - An overview of regularisation and ridge regression
What are the two perspective for ...,
Ols what is the the set up for mu...,
How does ols perform when p 1 n 3
20
cards
Chapter 4.2: Regularisation - Lasso and constrained forms
What is the lasso 1,
What are the properties of the la...,
What is sort of the informal pred...
14
cards
Chapter 4.3: Regularisation - Concentration inequalities
What is the excess risk and how c...,
What are the three terms that est...,
What is markov s inequality 3
17
cards
Chapter 4.4: Regularisation- Advanced analysis of Lasso
What is the lasso optimisation pr...,
What is the prediction error of t...,
What is theorem 2 the slow rate o...
28
cards
Chapter 4.5: Regularisation- Extension and related other problems
Can we use a lq penalty for lasso 1,
How do lasso and ridge compare wh...,
What is the elastic net penalty 3
8
cards
Chapter 5.1 - Classification (Part II) - Maximal margin classifier and support vector classifier
What is the background behind sup...,
What is the definiton of a hyperp...,
How do we get separating hyperpla...
20
cards
Chapter 5.2 - Classification (Part II) - Kernel Methods
What is svm and how does it relat...,
How do we use basis expansion in ...,
If we use a basis expansion of cu...
24
cards
Chapter 6.1 - Cross validation, bootstrap and tree-based methods - Cross validation and bootstrap
What are resampling approaches 1,
What are we always trying to esti...,
Why can we use a test set for cro...
18
cards
Chapter 6.2 - Cross validation, bootstrap and tree-based methods - Tree-based methods
What are tree based methods 1,
How do we write a classification ...,
How do we go about finding cj 3
16
cards
Chapter 7.1 - Mathematics of statistical machine learning - PAC Bounds
Recap of statistical learning the...,
Recap decomposing excess risk 2,
Recap how can estimation error be...
11
cards
Chapter 7.2 - Mathematics of statistical machine learning - Concentration inequalities
How can the third term be decompo...,
What is the definiton of a sub ga...,
If z is a sub gaussian then how c...
8
cards
Chapter 7.3 - Mathematics of statistical machine learning - Finite hypotheses classes
What is the idea behind trying to...,
What theorem does this lead to 2,
What is the proof of this theorem 3
6
cards
Chapter 7.4 - Mathematics of statistical machine learning - From shattering to VC dimension for infinite classes
What do we have to consider in th...,
When does f curly shatters x1 xn 2,
For n 1 1 does f curly shatter an...
20
cards
Chapter 7.4 - Mathematics of statistical machine learning - Bounding Estimation error for infinite classes
What is theorem 4 regarding the b...,
Using the sauer shelah lemma what...,
How can we use these results to b...
6
cards
Chapter 7.5 - [NON-EXAMINABLE] Mathematics of statistical machine learning - Rademacher complexity
0
cards
Chapter 7.6 - Mathematics of statistical machine learning - Structural risk minimisation
What is structural risk minimisat...,
Ideally how do we choose pen 2,
How can we therefore bound the pe...
3
cards
Chapter 6.3: Cross-validation, boosttrap and tree-based methods - Boosting
Recap what is pac strong learnabi...,
What is the difference between ba...,
What was robert schapire s rough ...
12
cards
Chapter 8.1 - Topics in big data - Bag of little bootstraps
Recap what is the classical boots...,
What is the issue with the classi...,
What is the idea of blb 3
7
cards
Chapter 8.2 - Topics in big data - Implicit bias of neural networks
What is the classical picture of ...,
How does the interpolating regime...,
Considering binary classification...
6
cards
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ST420 - Statistical Learning and Big Data
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