1. Teaching
1.1. Theoretical Part:
- 2 x 2 hours/week online videos
- lecture activities
1.2. Practical Part: Lab and 3 group Coursework
- ANN - Artifical Neural Networks
- Decision Trees
- Support Vector Machines
2. Assessment
- 2h Exam(70%)
- Contents from all lectures examinable, as well as the relevant chapters in the two core text (Bishop and Duda & Hart)
- Guest lectures can be used as examples but not examinable in detail.
- Three questions - no optional question
- One coursework (30%)
- Reports on 3 Lab topics
- Peer-review evaluation
3. Resources
Core text:
- Machine Learning & Pattern Recognition (Christopher Bishop)
Additional Reading:
- Machine Learning (Tom Mitchell)
- Pattern Classification (Duda & Hart)
4. Structure
- Introduction
- Linear Regression
- Mathematics for Machine Learning
- Linear Classification
- Decision Trees
- Artificial Neural Networks I
- Artificial Neural Networks II
- Deep Learning and Convolutional Neural Networks
- Evaluating Hypotheses
- Unsupervised Learning
- Data mining
- Dimensionality Reduction
- Kernel Methods
- Support Vector Machines
- Bayesian Learning
- Probabilistic Graphical Models
- Sequential Data
- Advanced Topics
- Exam Revision