Methods & Algorithms
MSc Data Science - Finance Applications
6Lectures40PDFs67Charts14Notebooks265Quiz Questions7Handouts1Assignment
P
Prerequisites
Foundational Knowledge -- Review these mini-lectures before starting the course. They cover linear algebra, ML paradigms, and classification/decomposition concepts assumed throughout L01-L06.
I
Regression
II
Instance & Ensemble Methods
III
Advanced Topics
L01
Introduction & Linear Regression
Overview PDF
Print-friendly
Deep Dive PDFFull theory PDF
Mini-Lecture PDF10-slide mini
PDF
Linear Regression Full
Colab NotebookOpen in Colab
Datasethousing_synthetic.csv
Charts (8)
L02
Logistic Regression
Overview PDF
Print-friendly
Deep Dive PDFFull theory PDF
Mini-Lecture PDF10-slide mini
Full Lecture PDF31-slide technical
Self-Study Guide5-page reading
Intuitive Guide5-page intro reading
Colab NotebookOpen in Colab
Datasetcredit_synthetic.csv
Charts (7)
L03
KNN & K-Means Clustering
Overview PDF
Print-friendly
Deep Dive PDFFull theory PDF
KNN Mini-Lecture10-slide standalone
K-Means Mini-Lecture10-slide standalone
KNN Full Lecture25-slide KNN theory
K-Means Full Lecture25-slide K-Means theory
Overview (Accessible)28-slide intro-level
Deep Dive (Accessible)29-slide intro-level
Combined NotebookKNN + K-Means
KNN NotebookVisual-heavy KNN
K-Means NotebookVisual-heavy K-Means
Datasetcustomers_synthetic.csv
Charts (13)
L04
Random Forests
Overview PDF
Print-friendly
Deep Dive PDFFull theory PDF
Mini-Lecture PDF10-slide RF overview
Full Lecture PDF31-slide RF theory
DT Mini-Lecture PDF10-slide DT overview
DT Full Lecture PDF25-slide DT theory
Combined NotebookDT + RF Combined
DT NotebookVisual-heavy DT
RF NotebookVisual-heavy RF
Datasettransactions_synthetic.csv
Charts (20)
Decision Tree
Feature Importance
Bootstrap
OOB Error
Ensemble Voting
Single Tree Variance
RF Variance Reduction
Decision Flowchart
Gini Split
DT Overfitting
DT Decision Boundary
Gini vs Entropy
Nonlinear Classes
AdaBoost Staged Error
GB Residuals
Boosting Learning Rate
Regression Tree MSE
Bias-Variance Depth
RF max_features
Pruning CCP
L05
PCA & t-SNE
Overview PDF
Print-friendly
Deep Dive PDFFull theory PDF
PCA Mini-Lecture10-slide PCA overview
t-SNE Mini-Lecture10-slide t-SNE overview
PCA Full Lecture25-slide PCA theory
PCA Simple Narrative25-slide chart-first PCA
t-SNE Full Lecture25-slide t-SNE theory
Visual Guide25-slide no-formula PCA+t-SNE
t-SNE Visual Deep Dive18-slide formula-free t-SNE
Top-20 Charts27-slide visual reference
Combined NotebookPCA + t-SNE Combined
PCA NotebookVisual-heavy PCA
t-SNE NotebookVisual-heavy t-SNE
Datasetportfolio_synthetic.csv
Charts (7)
L06
Embeddings & Reinforcement Learning
Overview PDF
Print-friendly
Deep Dive PDFFull theory PDF
Embeddings Mini10-slide Word2Vec
RL Mini-Lecture10-slide Q-learning
Embeddings Full Lecture25-slide embedding theory
RL Full Lecture25-slide RL theory
Embeddings Visual Guide16-slide no-formula embeddings
RL Visual Guide17-slide no-formula RL
LLM Visual Guide25-slide embeddings-to-LLMs
Modern Embeddings Guide11-slide practical guide
Modern RL Guide11-slide practical guide
Embeddings Complete39-slide definitive reference
RL Complete43-slide definitive RL reference
RL Big Picture42-slide concept guide, no formulas
Embeddings BSc38-slide beginner guide, no math
Top-20 Charts27-slide visual reference
Combined NotebookEmbeddings + RL Combined
Embeddings NotebookVisual-heavy embeddings
RL NotebookVisual-heavy Q-learning
Datasettext_corpus_synthetic.json
Charts (7)
GA
Group Assignment (60% of Grade)
ML Pipeline Challenge — Groups of 2-3 apply 5 of 6 course topics to a self-sourced dataset. Harder topic combinations earn higher scores through a difficulty multiplier.
5
Topics Required
Topics Required
60%
of Course Grade
of Course Grade
100
Points Total
Points Total
Topic Difficulty Points
| Topic | Points | If Omitted |
|---|---|---|
| L01 Linear Regression | 1 | Multiplier 1.00 |
| L02 Logistic Regression | 1 | Multiplier 1.00 |
| L03 KNN & K-Means | 2 | Multiplier 0.96 |
| L04 Random Forests & Boosting | 2 | Multiplier 0.96 |
| L05 PCA & t-SNE | 3 | Multiplier 0.92 |
| L06 Embeddings & RL | 4 | Multiplier 0.88 |
H
Pre-Class Discovery Handouts -- 43 activities across 6 lectures + 1 bonus
L01Linear Regression
6 activities -- Data & prediction
L02Logistic Regression6 activities -- Classification
L03KNN & K-Means7 activities -- Neighbors & clusters
L04Random Forests6 activities -- Trees & ensembles
L05PCA & t-SNE6 activities -- Dimensionality
L06Embeddings & RL6 activities -- NLP & agents
DLDeep Learning6 activities -- Neural networks
Q
Canonical Quizzes -- 120 questions (20 per lecture)
L01Linear Regression
20 questions -- Bloom L3+ 65%
L02Logistic Regression20 questions -- Bloom L3+ 70%
L03KNN & K-Means20 questions -- Bloom L3+ 60%
L04Random Forests20 questions -- Bloom L3+ 65%
L04Random Forests (Simple)10 questions -- Basics
L05PCA & t-SNE20 questions -- Bloom L3+ 65%
L05PCA Basics (Simple)20 questions -- Basics
L05t-SNE Basics (Simple)20 questions -- Basics
L06Embeddings & RL20 questions -- Bloom L3+ 65%
L06Embeddings Basics (Simple)20 questions -- Basics
L06RL Basics (Simple)20 questions -- Basics
DD
Deep Dive Quizzes -- 100 supplementary questions
OV
Overview Quizzes -- 45 cross-topic questions
Methods & Algorithms - MSc Data Science