Machine Learning (2019-2020)

Dr Ning Wang
Course Term: 
Course Overview: 

The course gives a brief introduction to the some of the basic concepts and tools in Machine Learning from a practical angle. The numerical examples and the practicals are based on programming languages Python.

Course Syllabus: 

+ Regularised regression (Ridge regression, LASSO) and cross validation
+ Classification techniques (Logistic regression, Classification trees, Support vector machines)
+ Combination of predictors (Bagging, Random forests, AdaBoost).
+ Neural networks (Backpropagation, Deep neural networks)
+ Reinforcement learning (Q learning, Deep reinforcement learning)

Reading List: 

+ The Elements of Statistical Learning by T. Hastie, R. Tibshirani and J. Friedman, Second edition, Springer, 2009
+ Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville – 3 Jan 2017
+ Reinforcement Learning: An Introduction By Richard S. Sutton and Andrew G. Barto, 1998

Please note that e-book versions of many books in the reading lists can be found on SOLO and ORLO.

Further Reading: 

+ TensorFlow for Deep Learning by Bharath Ramsundar and Reza Bosagh Zadeh – 28 Feb 2018
+ Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron