Wk

Dates

Topic

Ass

Lecturer

1

8

Introduction

Ass1 out

Will

July

Introduction to the course

Overview of five tribes of AI and tool use within them.

Weka-AB.pdf: WEKA tutorial slides

2

15

Problem types I

Bing

Pipeline for tools (TPOT, Clementine,…)

Tasks: optimisation, regression, classification, clustering, exploration

Resources (Webpages, blogs, tutorials, MOOCs)

Keel-AB.pdf: Introductory Keel tutorial slides

Weka-KF-AB.pdf: Weka Knowledge Flow tutorial slides

3

22

Problem types II

Will

Given a problem type, what properties should an algorithm possess?

No free lunch bounds

Keel-AB-Details Tutorial

Ass1 in

4

29

Data Engineering I

Ass2 out

Will


CRISP-DM

Data cleansing and Data pre-processing

Weka Data And Feature Explorer tutorial

5

5

Data Engineering II

Will

 Aug

Feature selection & Feature manipulation

Imputation: standard techniques

Weka-KF-AB-Part2 Tutorial ASSIGNMENT 2

6

12

Interpretation and Visualization

Ass2 in

Will

Evaluation: Analysis and measurement

Visualisation

Kaggle: Assignment tutorial

Ass3 out

MTB

MTB

7

2

Environmental interaction

Bing

Sept

Performance Metrics

MetricOptimisation

Tutorial

Ass3 in

Ass4 out

8

9

Tool Platforms

Bing

HardwareSoftware

Scikit-learn

Tutorial: a classification project with EDA using NumPy, Pandas, Matplotlib, and Scikit-learn

9

16

Tensor flow

Bing

Introduction to DeepLearning

Tools and Tensorflow

Tutorial

Other useful links: (CF MXNet, Deeplearning4j, TensorFlow, Microsoft Cognitive Toolkit or Theano) Building blocks such as layers, objectives, activation functions, optimizers,

Beliefs, priors, likelihoods, posterior and expectations.

*Project Out*

10

23

Keras

Bing

Deep Learning for Image Classification

Introduction on Keras

*Ass4 in*

11

30

State-of–the-art Tools

Will

Deep Convolutional Neural Network for Image Classification

More on Deep_Learning

12

7

Reality Check

Will

Oct

AI Ethics

Reality Check on Machine Learning - Success, Limits and Potentials

15

28

Project in