Keynote
L1 - Introduction to Machine Learning.key
PDF
L1 - Introduction to Machine Learning.pdf
Keynote
L2 - Decision Trees.key
PDF
L2 - Decision Trees.pdf
Keynote
L3 - Basic Concepts.key
PDF
L3 - Basic Concepts.pdf
Keynote
L4 - Nearest Neighbours.key
PDF
L4 - Nearest Neighbours.pdf
Keynote
L5 - Perceptron.key
PDF
L5 - Perceptron.pdf
Keynote
L5b - Functions and Objects in Python Primer.key
PDF
L5b - Functions and Objects in Python Primer.pdf
Keynote
L6 - Features and Evaluation.key
PDF
L6 - Features and Evaluation.pdf
Keynote
L7 - Linear Models.key
PDF
L7 - Linear Models.pdf
Keynote
L8 - Probabilistic Modeling.key
PDF
L8 - Probabilistic Modeling.pdf
Keynote
L9-10 - Neural Networks.key
PDF
L9-10 - Neural Networks.pdf
Keynote
L11 - Kernel Methods.key
PDF
L11 - Kernel Methods.pdf
Keynote
L12 - Unsupervised Learning.key
PDF
L12 - Unsupervised Learning.pdf
Keynote
L13 - Mixture Models and EM.key
PDF
L13 - Mixture Models and EM.pdf
Keynote
L14 - EM in General.key
PDF
L14 - EM in General.pdf
Keynote
L15 - Dimensionality Reduction.key
PDF
L15 - Dimensionality Reduction.pdf
Keynote
L16 - Deep Learning.key
PDF
L16 - Deep Learning.pdf
PDF
L16 - Deep Learning.pdf
PowerPoint
L16 - Deep Learning.pptx
Keynote
L17 - Revision.key
PDF
L17 - Revision.pdf
Excel
Neural Networks - XOR Demo.xlsx
PDF
What is F20ML.pdf