Machine Learning

Machine Learning is a fundamental aspect of data analytics that automates analytical model building in modern business. In the big data era, managers are able to use very large and rich data sources and to make business decision based on quantitative data analysis. Machine Learning covers a range of state-of-the-art methods/algorithms that iteratively learn from data, allowing computers to find hidden patterns and relationships in such data so as to support business decision. Emphasis is placed on applications involving the analysis of business data. Students will practise applying machine learning algorithms to real world datasets by using an appropriate computing package in Python.

Learning unit outcome:

On successful completion of this course you should be able to:
Have a deep understanding on different types of learning algorithms and can identify advantages and limitations of each method.
Build a strong machine learning skill set for business decision making.
Create machine learning models for studying relationship amongst business variables.
Work with various data sets and identify problems within real-world constraints.
Demonstrate proficiency in the use of statistical software, e.g. Python, for machine learning models implementation.

This unit introduces modern machine learning techniques and builds skills in using data for everyday business decision making.

Machine Learning Foundation
Modern Regression Methods
a. Ridge
b. Lasso
c. Elastic net
Advanced Classification Techniques
a. Logistic regression
b. Decision trees
Ensemble algorithms
a. AdaBoosting
b. Random Forest
c. XGBoost
Time series forecasting
a. Weighted average
b. Holt winters
c. LSTMs
KNNs
a. Regression
b. Classification
Artificial Neural Networks
a. Regression
b. Classification
Unsupervised learning
a. Kmeans clustering

Techniques we will be covering:

Model selection
Regularization
Hyper-parameter selection and tuning
Cross validation
Train, validation and test set