All lecture content sans the assignment(s) and important announcmenets will be hosted on the lectures Github Page which you can find it here.
Welcome to the lecture materials for use in B.Sc - Data Science and Machine Learning II where our focus will be on the topics of:
- The types of ML techniques used in industry,
- What methods are available to use for which situation,
- Understading the current programming libraries for ML & AI,
- A Detailed introduction to the world of Neural Networks.
The details of the lecture are given below.
| DESCRIPTION | VALUE |
|---|---|
| Program Name | B.Sc "Mechatronics Design & Innovation" |
| Module Name | Machine Learning & Data Science II |
| Semester | 5 |
| Room | Lecture Room |
| Assessment(s) | Midterm Assignment (40 %) Final Exam (60 %) |
| Lecturer | Daniel McGuiness |
| Software | Python |
| Hardware | - |
| SWS Total | 3 (2 + 1) |
| Total Units | 30 |
| ECTS | 5 |
| Lecture Type | ILV |
There will be one (1) assignment and (1) exam for this course.
The grade breakdown is as follows:
| DEFINITION | GRADE (%) |
|---|---|
| Individual Assignment | 40 |
| Final Examination | 60 |
| Sum | 100 |
An individual assignment will be given to you to work on. This assignment will consist of questions pertaining to concepts of Machine Learning and how to implement them on Python.
NOTE: The assignment is individual and is not meant to be worked as a group. Once the code and the work is submitted it will be vetted against a software to determine if any collusion has occured.
The Final exam will be done on the last session where questions covering the entire lecture will be asked. The duration of the exam will be ninety (90) and will be done written.
You are able to bring a calculator to the exam but no personal reference sheets are allowed. Any reference documents (if needed) will be provided for you during the beginning of the exam.
As it currently is, the lecture covers topic on the overview on ML with a heavy emphasis of programming first. The lecture approaches ML with emphasis on coding the problem and observing effect and then understanding the reasoning behind it.
The structure of the lecture is shown below.
| TOPIC | DESCRIPTION | SESSION |
|---|---|---|
| The Machine Learning Landscape | A Brief Overview on the landscape of ML along with its categories and major topics | 1 |
| End to End ML Project | An example application of a ML project without getting drowned in detail. | 2 |
| Classification | An introduction into different types of classification used in ML | 3 |
| Training Models | An overview of different types of Regression modelling and Gradient descents methods. | 4 |
| Support Vector Machines | Supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. | 5 |
| Desicion Trees | A look into the decision support hierarchical model that uses a tree-like model of decisions and their possible consequences, | 5 |
| Ensemble Learning and Random Forests | An overview of an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time | 5 |
| Dimensionality Reduction | A brief overview on Dimensionality Reduction, a method for representing a given dataset using a lower number of features (i.e. dimensions) while still capturing the original data's meaningful properties. | 6 |
| Unsupervised Learning Techniques | Focus on the methods used for clustering such as k-means, gaussian mixture and other methods | 6 |
| Intoruction to Artificial Neural Networks | An introduction to Artificial Neural Networks with examples and execises. | 7 |
The Code supplement is a Github webpage dedicated to hosting all the relevant code used in the lecture as it is not feasible to fit all the content of the code to the slides and it is easier to share this way.
Visit the Code Supplement Website
The following materials are recommend reading for the coure but by no means are they mandatory.
| TITLE | AUTHOR | PUBLISHER |
|---|---|---|
| AI and Machine Learning for Coders | Moroney L. | O' Reilly |
| Neural Networks and Deep Learning | Aggarwal S. | Springer |
| Python Machine Learning | Raschka., et. al. | Packt |
| Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow | Geron A. | O' Reilly |
| Machine Learning with Python Cookbook | Albon C. | O' Reilly |
| CS229 Lecture Notes | Ng A., et.al | - |
| Lecture Notes on Machine Learning | Migel A., et. al | - |
–DTMc