Midterm evaluation

This week’s topics

Midterm evaluation

1. Midterm assessment of the criteria taught so far
2. Individual feedback and targets

What you will be able to do

• 1.1 Define artificial intelligence and describe the different approaches to learning.
• 1.2 Describe a range of simple and complex machine learning strategies.
• 1.3 Explain the difference between supervised, unsupervised, and reinforcement learning.
• 1.4 Analyse the significance of the bias-variance trade-off in machine learning.
• 2.1 Describe how backpropagation is used to update neural network parameters to achieve a desired goal.
• 2.2 Explain the bias-variance trade-off in machine learning.
• 2.3 Explain the importance of loss functions in providing an objective for the training of neural networks.
• 2.4 Analyse and compare common optimisation strategies for neural network training.
• 2.5 Evaluate the advantages and disadvantages of deep and shallow neural network architectures.

Activity

Midterm assessment task and individual feedback.

Study materials