• Apply common operations (pre-processing, plotting, etc.) to datasets using Python.
• Explain the concept of supervised, semi-supervised, unsupervised machine learning and reinforcement learning.
• Explain how various supervised learning models work and recognize their limitations.
• Analyze which factors impact the performance of learning algorithms.
• Apply learning algorithms to datasets using Python and Scikit-learn and evaluate their performance.
• Optimize a machine learning pipeline using Python and Scikit-learn.

Topic 1: Introduction

This is an introduction to the course with an overview of the topics. We give a brief introduction to machine learning and its different variants.

• Why use machine learning?

• Machine learning basics and terminology

• The biggest challenge in machine learning

• Machine learning frameworks: supervised, semi-supervised, unsupervised and reinforcement learning

Topic 2: Regression

We will make a gentle start with regression. In the regression setting, a machine learning model will need to predict a number.

• The regression setting and its assumptions

• The mean squared error (MSE) and mean absolute error (MAE)

• Outliers in regression

• Linear regression and K-nearest neighbour regression

Topic 3: Classification

In classification, a machine learning model will need to predict a category or class.

• Terminology and basics of classification

• Building classifiers using histograms, nearest mean (nearest medoid) classifier, K-nearest neighbour (KNN) classifier

• The Bayes classifier and the Bayes error

• How to use the KNN classifier in practice

Topic 4: Training Models

Gradient descent is an iterative procedure to train models, such as logistic regression and neural networks.

• The basics of gradient descent

• The three variants of gradient descent: batch, mini-batch and stochastic gradient descent (SGD)

• How to tune gradient descent

• The basics of logistic regression

Topic 5: Overfitting

Overfitting is the problem where a machine learning algorithm performs well on the training set but does not perform well on new and unseen data.

• How to use linear models for nonlinear tasks?

• The bias-variance trade-off and the curse of dimensionality

• How to use learning curves to estimate the amount of data needed

Topic 6: Cross Validation & Regularization

To get a good estimate of the performance of machine learning models, cross validation is an essential technique. This is also important to tune hyperparameters of models. Finally, we discuss regularization, a technique that aims to avoid overfitting.

• Cross validation, model selection and hyperparameter tuning

• Ridge regression

• LASSO regularization and how it’s used for variable selection

Topic 7: Classifier Evaluation

Classifier evaluation delves deeper into the various evaluation metrics for classifiers.

• What a “good” accuracy means (e.g., naïve baselines/dummy classifiers)

• The confusion matrix (false positive, false negative, costs)

• ROC-curves

Topic 8: Support Vector Machines

The support vector machine is a well-known more advanced classification model.

• Basics of the SVM, the margin and the hard-margin SVM

• The soft-margin SVM

• Kernels

Topic 9: Decision Trees

Decision trees are simple and interpretable models that are very user-friendly.

• Basics of decision trees and their terminology

• How to train decision trees with CART

• Overfitting and other pros and cons of decision trees

Topic 10: Final Project

The final project will involve building a machine learning pipeline, including hyperparameter tuning and a careful and fair evaluation, to solve a small practical application, that is the recognition of handwritten digits (MNIST).

"I enrolled in this course to challenge myself and expand my horizons. It's a step towards fulfilling my aspirations.

The course was a transformative experience that reshaped my career trajectory. It not only deepened my expertise but also broadened my professional network. Learning online was a incredibly flexible and convenient. The ability to access a wealth of resources and interact with peers globally, from the comfort of my home, was truly remarkable"

- Cheerla Balaji Srinivasu, Student from India