Machine Learning Guidance

A collection of my Machine Learning and Data Science Projects,

• including both Theoretical and Practical (Applied) ML,
• references (paper, ebook, repo, tool, etc), ranging from beginner to advanced.

Generative vs Discriminative Model

Given the training data set $D = { ( x_i ; y_i ) | i ≤ N ∈ Z }$, where $y_i$ is the corresponding output for the input $x_i$.

Aspect\Model	Generative	Discriminative
Learn obj	$P(x,y)$ Joint probability	$P(y\vert x)$ Conditional probability
Formulation	class prior/conditional $P(y)$, $P(x\vert y)$	likelihood $P(y\vert x)$
Result	not direct (Bayes) $P(y\vert x)$	direct classification
Examples	Naive Bayes, HMM	Logistic Reg, SVM, DNN

Reference: Generative and Discriminative Model, Professor Andrew NG

Types of Learning

• Learner $L(X_I)=h \in \mathcal{H}$
  • Input training data $X_I$, where $x_i \in \R$
  • Hypothesis $h_{\omega}: X \in R^n \rightarrow Y$, with weights $\omega$.
    • mapping attributes vectors $X$ to labels/output $Y={y_1,...,y_n}$
    • For NN, $h(x)=f(\omega;x)$, explicitly parameterized by $\omega$
    • For Generative model $f: Z \rightarrow X$, $Z$ is the latent variable

Output \ Type	Unsupervised	Supervised
Continuous $Y$ $=\R$	Clustering & Dim Reduction	Regression
	○ SVD	○ Linear / Polynomial
	○ PCA	○ Non-Linear Regression
	○ K-means	○ Decision Trees
	○ GAN ○ VAE ○ Diffusion	○ Random Forest
Discrete $Y$ $={Categories}$	Association / Feature Analysis	Classification
	○ Apriori	○ Bayesian       ○ SVM
	○ FP-Growth	○ Logistic Regression ○ Perceptron
	○ HMM	○ kNN / Trees

And more,

Aspect \ Type	Semi-Supervised	Reinforcement
Learn from	Labels available	Rewards
Methods	pseudo-labels	○ Q learning
	iteratively	○ Markov Decision Process

Reinforcement Learning

• In a state each timestamp
  • when an action is performed, we move to a new state and receive a reward
  • No knowledge in advance of how actions affect either the new state or the reward

Goal

• Value-based V(s)
  • the agent is expecting a long-term return of the current states under policy π
• Policy-based
  • the action performed in every state helps you to gain maximum reward in the future
  • Deterministic: For any state, the same action is produced by the policy π
  • Stochastic: Every action has a certain probability
• Model-based
  • create a virtual model for each environment
  • the agent learns to perform in that specific environment

Geometric Deep Learning

The bigger picture of learning with invariances and symmetries:

Domain	Structure	Symmetry / Bias	Example
Images	2D grid	Translation equivariant	CNNs
Sequences	1D sequence	Order-aware	RNNs, Transformers
Sets / Point Clouds	Unordered set	Permutation invariant	Deep Sets, PointNet
Graphs	Nodes + edges	Permutation equivariant	GNNs, Graph Isomorphism Networks
Manifolds / Spheres	2D surface embedded in 3D	Rotation equivariant	Spherical CNNs

Feature Engineering

• Feature Selection
  • After fitting, plot Residuals vs any Predictor Variable
  • Linearly-dependent feature vectors
• Imputation
• Handling Outliers
  • Removal, Replacing values, Capping, Discretization
• Encoding
  • Integer Encoding
  • One-Hot Encoding (enum -> binary)
• Scaling
  • Normalization, min-max/ 0-1
  • Standardization

Inference

Aspect	Bayesianism	Frequentism
Interpretation of Probability	A measure of belief or uncertainty	The limit of relative frequencies in repeated experiments
Methods	Prior knowledge and updates beliefs (Bayes') to obtain posterior distributions	Hypothesis testing, MLE, confidence intervals
Treatment of Uncertainty Random Variables	Parameters	Data set
Handling of Data	useful when prior information is available or when the focus is on prediction intervals.	often requires larger sample sizes
Flexibility	flexible model, allow updating models for new data	more rigid, on specific statistical methods
Computational Complexity	can be intensive computation, for models with high-dim parameter spaces	simpler computation and may be more straightforward in practice

Empiricism

Applied ML Best Practice

DNN Troubleshooting

Basic

• Initial test set + a single metric to improve
• Target performance
  • Human-level performance, published results, previous baselines, etc.

Intuition

• Results can be sensitive to small changes in hyperparameter and dataset makeup.

                          Tune hyperparameter
                                  |
Start simple -> Implement & Debug -> Evaluate -> ?
                                  |
                         Improve model & Data

• Start simple: simplest model & data possible (LeNet on a subset of the data)
• Implement & Debug: Once model runs, overfit a single batch & reproduce a know result
• Evaluate: Apply the bias-variance decomposition
• Tuning: Coarse-to-fine random search
• Improve model/data
  • Make model bigger if underfit
  • Add data or regularize if overfit

Troubleshooting

OpenAI Talk

DNN improvements

Improvement direction

My Projects

Machine Learning Real World Data, University of Cambridge IA

• Text Classification; Naive Bayes; Cross-Validation,
• HMM; Social Network

Theoretical Machine Learning with Problems Sets, Stanford CS229

• Linear classifiers (Logistic Regression, GDA), SVM, etc
• Stochastic Gradient Descent; L1 L2 Regularization

Deep Learning for Computer Vision with Problems Sets, Stanford CS231n

• Image Classification + Localization $(x,y,w,h)$ [ Supervised Learning, Discrete label + Regression ]
  • kNN; Softmax; classifier SVM classifier; CNN
• Object Detection
• Semantic / Instance Segmentation
• Image Captioning
  • RNN, Attention, Transformer
  • Positional Encoding
• Video understanding
• Generative model (GAN, VAE)
• Self-Supervised Learning

See more: Visual Computing

More

• Data Science | Uni. of Cambridge, Undergraduate course.
• AI | Uni of Cambridge, IB
  • Search, Game, CSPs, Knowledge representation and Reasoning, Planning, NN.
• Machine Learning and Bayesian Inference | Uni of Cambridge, Undergraduate course.
  • Linear classifiers (SVM), Unsupervised learning (K-means,EM), Bayesian networks
• Geometric Deep Learning | Cambridge, Oxford Master's courses.

Reference

📝OpenAI cookbook

Generative Pre-trained Transformer (GPT) from Scratch (Andrej Karpathy)

• ▶Youtube
  • 👨‍💻Code Github repo
• 👨‍💻nanoGPT repo

Paper

• 📄Attention is All You Need
• 📄OpenAI GPT-3
• 📝OpenAI ChatGPT blog post
• etc.

Library

• Numpy, matplotlib, pandas, TensorFlow
• Caffe, Keras
• XGBoost, gensim