Introduction to machine learning with R PrerequisitesNew
Wed 1 Mar - Thu 2 Mar 2017
CANCELLED
Description
This course provides a broad introduction to machine learning. Several state-of-the-art machine learning algorithms will be presented, with a focus on classification techniques using KNN, decision trees and random forests.
Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.
Target audience
- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
- Please be aware that these courses are only free for University of Cambridge students. All other participants will be charged a registration fee in some form. Registration fees and further details regarding the charging policy are available here
- Further details regarding eligibility criteria are available here
Prerequisites
- Experience with R is recommended, as well as familiarity with matrices and basic statistics theory.
- We expect participants to have attended an introductory R course or have a working knowledge of R.
Sessions
Number of sessions: 2
| # | Date | Time | Venue | Trainer | |
|---|---|---|---|---|---|
| 1 | Wed 1 Mar 2017 09:30 - 17:00 | 09:30 - 17:00 | Bioinformatics Training Room, Craik-Marshall Building | map | Elena Chatzimichali |
| 2 | Thu 2 Mar 2017 09:30 - 17:00 | 09:30 - 17:00 | Bioinformatics Training Room, Craik-Marshall Building | map | Elena Chatzimichali |
Topics covered
Bioinformatics, Data mining, Machine learning
Objectives
After this course you should:
- Gain a good understanding of supervised and unsupervised learning techniques, and be familiar with some of their real-world applications
- Be familiar with the series of data importing, pre-processing, mining and visualising steps required in a Machine Learning pipeline
- Know how to build and thoroughly validate a prediction model, evaluate its performance metrics and ensure the statistical significance of the results
- Have the confidence to apply these techniques on new real-world case studies
Aims
During this course you will learn about:
- Supervised vs. Unsupervised Learning
- Data cleaning and pre-processing
- Data mining and visualisation (Exploratory Data Analysis)
- Feature extraction and dimensionality reduction
- Classification models:
- K Nearest Neighbours
- Decision Trees
- Random Forests and ensemble models
- Overfitting, bias-variance trade-off and validation techniques
Format
Presentations, demonstrations and practicals
Timetable
| Day 1 | Topics | Speaker |
| 09:30 - 10:00 | Introduction to Machine Learning | Elena Chatzimichali |
| 10:00 - 11:00 | R for Data Science (data structures, functions and plotting commands) | |
| 11:00 - 11:15 | Tea/Coffee Break | |
| 11:15 - 12:30 | Data mining, cleaning and pre-processing | Elena Chatzimichali |
| 12:30 - 13:30 | Lunch | |
| 13:30 - 14:30 | Exploratory Data Analysis – Data Visualisation | Elena Chatzimichali |
| 14:30 - 14:45 | Tea/Coffee Break | |
| 14:45 - 16:00 | Principal Component Analysis | Elena Chatzimichali |
| 16:00 - 16:30 | Test Activities | |
| 16:30 - 17:00 | Summary and Q & A | |
| Day 2 | ||
| 9:30 – 10:45 | K Nearest Neighbours Classifier | Elena Chatzimichali |
| 10:45 - 11:00 | Tea/Coffee Break | |
| 11:00 - 12:30 | Overfitting, bias-variance trade-off and hyperparameter tuning | Elena Chatzimichali |
| 12:30 - 13:30 | Lunch | |
| 13:30 - 14:15 | Decision Trees | Elena Chatzimichali |
| 14:15 - 14:30 | Tea/Coffee Break | |
| 14:30 - 16:00 | Ensemble models and Random Forests | Elena Chatzimichali |
| 16:00 - 16:30 | Test Activities | |
| 16:30 - 17:00 | Summary and Q & A | |
Registration Fees
- Free for University of Cambridge students
- £ 50/day for all University of Cambridge staff, including postdocs, and participants from Affiliated Institutions. Please note that these charges are recovered by us at the Institutional level
- It remains the participant's responsibility to acquire prior approval from the relevant group leader, line manager or budget holder to attend the course. It is requested that people booking only do so with the agreement of the relevant party as costs will be charged back to your Lab Head or Group Supervisor.
- £ 50/day for all other academic participants from external Institutions and charitable organizations. These charges must be paid at registration
- £ 100/day for all Industry participants. These charges must be paid at registration
- Further details regarding the charging policy are available here
Duration
2 days
Frequency
A number of times per year
Related courses
Theme
Machine Learning
Booking / availability