Schedule
Week 1
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- Introduction to ML
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- Introduction to Classification
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- 22 Sep
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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Week 2
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- Naive Bayes Classification
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- Logistic Regression
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- 29 Sep
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Lab 0 Introduction to Python and ML
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Week 3
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- Linear Regression
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- Decision Trees
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- 06 Oct
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Lab 1 Classification, Naive Bayes, and Logistic Regression
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Week 4
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- Representing Data
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- Exploratory Data Analysis
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- CW 1 Start
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- 13 Oct
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Tutorial 1 Classification and Naive Bayes
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Week 5
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- Optimisation
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- Generalisation
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- 20 Oct
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Lab 2 Exploratory Data Analysis, Visualisation, and PCA
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Week 6
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- Evaluation
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- Model Selection
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- 27 Oct
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Tutorial 2 Optimization and Logistic Regression
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Week 7
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- Clustering
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- Non-Linear Dimensionality Reduction
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- 03 Nov
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Lab 3 Evaluation
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- 04 Nov
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- CW 1 Progress report due Wed 04 Nov, 5pm
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- 06 Nov
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- CW 1 Progress feedback Fri 06 Nov, TBD
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Week 8
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- Recommender Systems
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- Neural Networks
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- 10 Nov
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Tutorial 3 Model Selection
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Week 9
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- Ethics and Fairness
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- Further Topics
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- 17 Nov
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- Lab 4 Neural Networks
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Week 10
- 24 Nov
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- Q&A Session (16:10 @ AT - Lecture Theatre 5)
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- CW 1 Due
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- Tutorial 4 Ethics and Fairness
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