Overview of Machine Learning Techniques

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AI or ML or what?

• It’s not intelligent. It’s just machine learning which is statistics.
• Artificial intelligence is a marketing term, but it’s supported in literature as the high level term.
• That’s OK!
• I’m mainly going to use AI from here in.

Machine Learning Techniques Overview

Supervised Learning

• Concept: Techniques where models learn from labeled data.
• Explain: Like teaching a child with clear examples and answers.

1️⃣ Support Vector Machines (SVM)

• Description: Uses hyperplanes for classification.
• Explain: Think of drawing lines to separate different types of objects.
• Paper: A comprehensive survey on support vector machine classification

2️⃣ Naive Bayes

• Description: Probabilistic classifier based on Bayes’ Theorem.
• Explain: Like guessing the likelihood of something happening based on past events.
• Paper: An Empirical Study of the Naïve Bayes Classifier

3️⃣ Linear Regression

• Description: Models linear relationships between variables.
• Explain: Like predicting your height based on your age.
• Medium Post: A short into to Linear Regression

4️⃣ Logistic Regression

• Description: Used for binary classification problems.
• Explain: Like deciding if something is true or false.
• Paper: Logistic regression in data analysis: An overview

5️⃣ Decision Trees

• Description: Tree-like model for decisions and consequences.
• Explain: Like a flowchart to make decisions, but using numbers (weights)
• Paper: Study and Analysis of Decision Tree Based Classification Algorithms

6️⃣ Random Forest

• Description: Ensemble of decision trees for improved accuracy.
• Explain: Like consulting a group of experts instead of just one.
• Paper: Understanding Random Forests: From Theory to Practice

Unsupervised Learning

• Concept: Techniques where models learn from unlabeled data.
• Explain Like I’m New: Learning without direct guidance, like exploring a new city without a map.

1️⃣ K-Means Clustering

• Description: Partitions data into k distinct clusters.
• Explain Like I’m New: Like organizing similar things into different groups.
• Paper: K-means clustering algorithms: A comprehensive review, variants, and advances

2️⃣ K-Nearest Neighbors (KNN)

• Description: Classifies cases based on similarity measures.
• Explain Like I’m New: Like making friends based on common interests.
• Paper: Comparative performance analysis of K-nearest neighbour (KNN)

Neural Networks and Deep Learning id:: 659a9232-2320-494a-b922-968029718ad5

• Concept: Advanced algorithms inspired by the structure of the human brain.
• Explain: Like building a brain in a computer to solve complex problems.

1️⃣ Neural Networks

• Description: Consists of layers of interconnected nodes which just tweak numbers
• Explain: Like a network of brain cells working together to think and learn.
• Paper: Neural networks: An overview of early research, current frameworks and new challenges

2️⃣ Deep Learning

• Description: Involves training large neural networks.
• Explain: Larger scale engineering of neural nets, to solve much harder problems.
• Paper: Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, and Applications
• Fine tuning and alignment

3️⃣ Reinforcement Learning from Human Feedback RLHF

• Description: Two neural networks trained in an adversarial process.
• Explain: Like two brains, one creating art and the other judging it, helping each other improve.
• Paper: Generative Adversarial Networks

4️⃣ Direct Preference Optimisation DPO

• Description: *DPO dramatically simplifies the whole thing.
• Explain: Removes the reward function, and so the human in the loop.
• Paper: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (arxiv.org)
• In operation: Proprietary Large Language Models:

5️⃣ Generative Adversarial Networks GANs

• Description: Two neural networks trained in an adversarial process.
• Explain: Like two brains, one creating art and the other judging it, helping each other improve.
• Paper: Generative Adversarial Networks

6️⃣ Diffusion Models (Generative Models)

• Description: Advanced models that ‘diffuse’ data to create new, synthetic outputs, using efficient Transformers
• Explain: Imagine starting with a noisy, random pattern and gradually shaping it into a clear picture.
• Paper: Diffusion Models: A Comprehensive Survey of Methods and Applications (Note: This covers the lot including:)

7️⃣ 🟢 Transformers

• Description: Circa 2017, introduced self-attention mechanism to capture dependencies between different words in a sequence.
• Explain: Examines the interdependencies across a wider view of words / tokens
• Paper: Attention Is All You Need (arxiv.org) (underpinned recent advances)
• Not the only game in town State Space and Other Approaches and others

• Next presentation slide Proprietary Large Language Models