The course will provide an overview of fundamental concepts and algorithms in machine learning. Mathematical and coding exercises will be provided to deepen understanding of these concepts and algorithms.
Material
- Introduction to machine learning and its applications
- Supervised learning: linear and nonlinear regression
- Supervised learning: logistic regression and multi-class classification
- Introduction to neural networks
- Multi layer perceptrons (MLPs)
- Activation functions
- Feedforward neural networks and backpropagation
- Introduction to convolutional neural networks for computer vision
- Image representations
- Convolutional operators and kernels, max pooling, dense layers, depth vs width
- Dimension calculations and parameter count
- Popular datasets and model zoos
- Challenges: Transfer learning and Adversarial attacks
Instructor
- Dr. Adel Bibi (Email: adel.bibi@eng.ox.ac.uk, bibiadel93@gmail.com (preferred))
Tutorial
- Cornelius Emde (Email: cornelius.emde@exeter.ox.ac.uk)
Notes
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NOTE 1: We will go through the following content in 4 days. We will use [google colab] as a platform for exercises.
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NOTE 2: for each colab file below, with a preappended title
Exercise:, you should make a copy in your own google drive to edit & run. -
NOTE 3: The coffee break over the four days will always be from 10:30AM to 11:00AM.
Background Review
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Colab/Markdown/latex intro [env intro]
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Coding exercise: Python Basics [Exercise: Numbers] [Exercise: Strings] [Exercise: Lists] [Exercise: Dictionaries] [Exercise: Tuples] [Exercise: IF,FOR, and While] [Exercise: Numpy]
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For background review on linear algebra and probability, read the following course
Lectures
Day 1: August 19th, 2024
- Lecture #1: AI and Machine Learning Applications
- Lecture #2: Regression
- Lecture #3: Logistic regression
- Excercise #1: Regression, Solutions
Day 2: August 20th, 2024
- Lecture #4: Neural Networks
- Exercise #2: Logistic regression, Solutions
- Exercise #3: Neural Networks, Solutions
Day 3: August 21st, 2024
- Lecture #5: Convolutional Neural Networks (1)
- Exercise #4: Training a ConvNetwork, Solutions
Day 4: August 22nd, 2024
- Lecture #6: Convolutional Neural Networks (2)
- Exercise #5: Transfer learning
- Exercise #6: Adversarial attacks
Acknowledgments
Thanks to Hasan Hammoud for the help in preparing some exercises