CS代考程序代写 algorithm LECTURE 4 TERM 2:

LECTURE 4 TERM 2:
MSIN0097
Predictive Analytics
A P MOORE

SYSTEMS DESIGN
Original problem

DEALING WITH DIFFICULT PROBLEMS
– Buildupabettersolutionbythinkingabouthowpartialsolutionscan support/correct each others mistakes

DEALING WITH DIFFICULT PROBLEMS
– Buildupabettersolutionbythinkingabouthowpartialsolutionscan support/correct each others mistakes
— Make the problem simpler – Divideandconcur
– Problemdecomposition

DEALING WITH DIFFICULT PROBLEMS
– Buildupabettersolutionbythinkingabouthowpartialsolutionscan support/correct each others mistakes
— Make the problem simpler – Divideandconcur
– Problemdecomposition
— Building much better solutions – Deepmodels

ENSEMBLES
— Build up a better solution by thinking about how partial solutions can support/correct each others mistakes

ENSEMBLES
— Voting
– Majorityvoting
— Bagging and Pasting
– Out-of-bagevaluation
— Boosting
– XGBoost
— Stacking

MAJORITY VOTING

B AGGING

DECOMPOSITION
STARTING WITH EASIER PROBLEMS
— Break the problem into a lot of easier sub-tasks

A – B – C- D ALGORITHMIC APPROACHES
A. ClAssification
B. Regression
Super vised
C. Clustering
D. Decomposition
Unsuper vised

A – B – C- D ALGORITHMIC APPROACHES
A. ClAssification
B. Regression
We know what the right answer is
Super vised
C. Clustering
D. Decomposition
Unsuper vised

A – B – C- D ALGORITHMIC APPROACHES
A. ClAssification
B. Regression
Super vised
C. Clustering
D. Decomposition
Unsuper vised
We don’t know what the right answer is – but we can recognize a good answer if we find it

A – B – C- D ALGORITHMIC APPROACHES
A. ClAssification
B. Regression
Super vised
C. Clustering
D. Decomposition
Unsuper vised
We don’t know what the right answer is – but we can recognize a good answer if we find it

MOTIVATING DECOMPOSITION

COMPRESSION

D. DECOMPOSITION 2. PROJECTION METHODS
Dimensionality reduction

D. DECOMPOSITION 2. KERNEL METHODS

D. DECOMPOSITION 3. MANIFOLD LEARNING

CURSE OF DIMENSIONALITY

SUBSPACES

MOTIVATING DECOMPOSITION

LOW DIMENSIONAL SUBSPACES

DECOMPOSITION
THREE APPROACHES
— Dimensionality Reduction / Projection — Kernel Methods
— Manifold Learning

B. REGRESSION REAL VALUED VARIABLE

MOTIVATING PROJECTION INSTABILITY

FINDING THE RIGHT DIMENSION

SUBSPACES

PROJECTION IN MULTIPLE DIMENSIONS

REDUCTION TO A SINGLE DIMENSION

COMPRESSION
MNIST 95% VARIANCE PRESERVED

PROBLEMS WITH PROJECTION

PROBLEMS WITH PROJECTION

KERNEL METHODS
Kernel spaces

KERNEL PCA

MANIFOLD METHODS
Manifold learning

MANIFOLD LEARNING

MANIFOLD LEARNING

OTHER TECHNIQUES

LOCAL LINEAR EMBEDDING

DECOMPOSITION METHODS
— Random Projections
— Multidimensional Scaling (MDS) — Isomap
— Linear Discriminant Analysis (LDA)

The main motivations for dimensionality reduction are:
— To speed up a subsequent training algorithm (in some cases it may even remove noise and redundant features, making the training algorithm perform better).
— To visualize the data and gain insights on the most important features. — Simply to save space (compression).