IAML-PG2: Schedule
Schedule
| Week | Topics | Lab | Small group tutorial | Coursework | Class meeting (Wednesday) |
|---|---|---|---|---|---|
| 0 | Probability Topics - math preliminaries - videos and slides | ||||
1
| Thinking About Data - videos and slides - quiz - answer | Lab 0: Introduction to Python and ML packages | (i) Course overview and activities, overall plan. Q&A on Labs, Assignments and Coursework (ii) Thinking about data Q&A (iii) Probability Q&A | ||
2
| Naive Bayes Decision Trees | Lab 1: Data Analysis and Visualisation and Multinomial Naive Bayes | (i): Bayes rule Q&A (ii): Naive Bayes Q&A (iii): Decision Trees Q&A | ||
3
| Generation Evaluation | Tutorial 1: Naive Bayes and Feature Engineering | (i): Generalisation and Evaluation Q&A | ||
4
| Linear Regression Logistic Regression | Lab 2: Decision Trees and Linear Regression | (i): Linear Regression Q&A (ii): Logistic Regression Q&A | ||
5
| Optimisation Regularization | Tutorial 2: Decision Trees and Gaussian Naive Bayes | (i): Optimisation and Regularisation Q&A | ||
6
| Support Vector Machines Ethics in ML | Lab 3: SVMs, Evaluation | (i): Support Vector Machines Q&A (ii): Ethics Q&A | ||
7
| Nearest Neighbours K-Means GMM | Tutorial 3: Logistic Regression | Coursework 1 will be released | (i): K-Means Q&A (ii): Gaussian Mixture Models Q&A (iii): Nearest Neighbour Q&A | |
8
| PCA Hierarchical Clustering | Lab 4: Clustering, PCA and Evaluation | PCA Q&A | ||
9
| Neural Networks - videos and slides | Tutorial 4: SVMs and Gaussian Mixture Models | Hierarchical Clustering Q&A | ||
10
| Revision Session | Lab 5: Neural Networks | (i) Neural Networks Q&A (ii) Revision class | ||
11
| No class meetings |
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