Table of Contents
Introduction to Machine Learning
Goals of Machine Learning
- Improve the performance of an intelligent agent over time
- Enable agents to adapt to new, unseen scenarios
- Automate complex decision-making without hand-coding every rule
- Extract actionable knowledge from raw data
Core Challenges
- Handling large, dynamic datasets that shift over time
- Identifying relevant features amid noise and irrelevant data
- Avoiding overfitting — learning the noise, not the signal
- Scaling algorithms to real-world complexity
Concept & Scope of ML
Machine Learning sits at the intersection of computer science, statistics, and domain knowledge. Its scope spans from simple linear models to deep neural networks — covering perception (recognising images, speech), prediction (credit scores, demand forecasting), and decision-making (game-playing agents, robotics).
Learning Paradigms
Intelligence is not monolithic. A truly intelligent agent must learn, search, and problem-solve — often simultaneously. These three pillars form the backbone of intelligent behaviour.
Learning
Acquiring new knowledge or skills from experience — through examples, feedback, or exploration of the environment.
Searching
Exploring a space of possible states or solutions to find the optimal path — from A* to Monte Carlo Tree Search.
Problem Solving
Formulating goals and executing action sequences to reach them — blending planning, reasoning, and learned heuristics.
Observation Phase
The agent passively collects data from the environment — sensor readings, user behaviour, game moves.
Induction Phase
Patterns are extracted from the raw data and distilled into a generalisable internal model or rule set.
Inference/Action Phase
The trained model is applied to new inputs — making predictions, taking actions, or solving novel tasks.
Feedback & Adaptation
The outcome is evaluated; the model is updated to correct errors and reinforce successful strategies.
Learning Concepts, Methods & Models
Machine learning encompasses a rich family of algorithms, each suited to different data types, task goals, and feedback mechanisms. Below we explore each method in depth.
Rote Learning
Learning through direct memorisation
How It Works
An agent stores every experienced situation and its corresponding optimal action in a lookup table. When the same situation recurs, the stored answer is returned instantly without any recomputation.
Characteristics
- ✔ Strength Perfect recall for previously seen inputs
- ✔ Strength Zero recomputation cost at inference time
- ✘ Weakness Cannot generalise to unseen situations
- ✘ Weakness Memory grows linearly with experience
- ✘ Weakness Fails when data is dynamic or context-dependent
Learning from Observations
Extracting patterns from recorded facts
The Observation Cycle
Key Principles
- ℹ Observations are recorded as a set of attribute-value pairs
- ℹ Patterns are discovered via inductive reasoning
- ℹ No explicit target label is provided during observation
- ℹ Works well for time-series and sensor data
Supervised Learning
Learning with labeled data and explicit feedback
f(X) → Y that generalises to unseen inputs.
Classification
Predicts a discrete label from a finite set of classes.
- 📧 Spam vs. Not-Spam email detection
- 🩺 Disease diagnosis (positive / negative)
- 💳 Credit default prediction (default / no default)
Common Algorithms: Decision Trees, Naive Bayes, SVM, Logistic Regression, k-NN
The Supervised Learning Pipeline
Unsupervised Learning
Finding hidden structure without labels
Clustering
Group similar data points together. e.g. k-Means, DBSCAN, Hierarchical Clustering.
Dimensionality Reduction
Compress high-dimensional data while preserving structure. e.g. PCA, t-SNE, Autoencoders.
Association Rule Mining
Discover co-occurrence rules in transactional data. e.g. Apriori algorithm, market basket analysis.
Generative Modelling
Learn the data distribution to generate new samples. e.g. GANs, Variational Autoencoders.
Semi-supervised Learning
A powerful hybrid of labeled and unlabeled data
Why it matters
In practice, collecting labeled data is expensive and time-consuming (think: manually annotating thousands of medical images). Semi-supervised learning leverages the cheap unlabeled data to significantly boost accuracy over a purely supervised approach.
Common Techniques
- Self-training — iteratively label confident predictions
- Co-training — two models teach each other
- Graph-based methods — label propagation
- Generative models — learn joint distribution
Real-World Applications
- 🏥 Medical image classification
- 📰 Text document categorisation
- 🗣️ Speech recognition
- 🌐 Web content classification
Ensemble Learning
Many weak learners, one strong result
Bagging
Train multiple models on random subsets of data, then aggregate (e.g. majority vote). Reduces variance.
Boosting
Train models sequentially; each focuses on the errors of its predecessor. Reduces bias.
Stacking
Use a meta-learner to learn how to best combine the outputs of base learners.
Discovery-based Learning
Inductively finding previously unknown, meaningful information
The KDD Process
Key Properties of Discovered Knowledge
- ★ Previously Unknown — not an obvious or trivial relationship
- ★ Valid — holds true with high confidence on the data
- ★ Useful — actionable in a business or scientific context
- ★ Understandable — interpretable by a human expert
Learning by Problem Solving
Improving performance through task execution and optimisation
The Learn-by-Doing Loop
Problem Encountered
Apply Stored Strategy
Evaluate Outcome
Update / Optimise Strategy
Connection to Reinforcement Learning
Learning by problem solving is the conceptual foundation of Reinforcement Learning (RL). The agent receives a reward signal after each action and uses it to refine its policy — the mapping from states to actions.
Knowledge Discovery vs. Machine Learning
These two fields are often conflated, but they have distinct goals and processes. Here is a definitive side-by-side comparison.
| Dimension | 📂 Knowledge Discovery | 🤖 Machine Learning |
|---|---|---|
| Primary Goal | Extract previously unknown patterns from data | Improve predictive performance on a task |
| Output | Understandable rules and associations | A trained model (can be a black box) |
| Human Interpretability | ⭐⭐⭐⭐⭐ Highest priority | ⭐⭐ Often secondary to accuracy |
| Feedback Required | ❌ Typically unsupervised | ✅ Usually requires labels |
| Data Volume | Very large databases (petabyte scale) | Varies — can be small (few-shot learning) |
| Primary Techniques | Association Rules, Clustering, Anomaly Detection | Neural Networks, SVMs, Decision Trees |
| Classic Example | Diaper-beer association in supermarkets | Image recognition via CNNs |
| Metrics | Support, Confidence, Lift | Accuracy, F1-score, AUC-ROC |
Bank Credit Risk: The Ram Example
This case study illustrates how ML predictions are made in a high-stakes financial setting.
The Scenario
Ram applies for a bank loan. The bank's ML system must decide: will Ram default on his loan repayment? The system doesn't know Ram personally — it must infer from patterns seen in thousands of historical loan applications.
Input Parameters (Features)
Multi-perspective Integrated Intelligence (MPI)
MPI is a theoretical framework that describes how a truly intelligent system integrates multiple cognitive perspectives — learning, reasoning, and problem-solving — rather than relying on a single, monolithic approach.
💼 The Job Application Analogy
Raw input data — CV, cover letter, interview performance.
The system observes and scores multiple dimensions: skills, experience, cultural fit.
A rejection letter arrives — but it contains specific, actionable feedback on what was lacking.
The candidate improves their skills, refines their CV, and applies again — now with higher success probability.
The iterative feedback loop — observe, reason, learn, act — mirrors how MPI agents improve.
The Three MPI Pillars
Perceptual Intelligence
The ability to observe and interpret raw data inputs from the environment with high fidelity.
Cognitive Intelligence
Reasoning, pattern recognition, and knowledge integration across domains and time steps.
Adaptive Intelligence
Learning from feedback and dynamically updating strategies to improve future performance.
Test Your Understanding
Challenge yourself with these seminar-style questions. Click an answer to see the explanation.