Here are a number of interesting courses, two of which I am looking at for the past two weeks and that i would hopefully finish by the end of August-September.
Introduction to Neural Networks (MIT):
These days, amongst the other things that I have at hand including a project on content based image retrieval. I have been making it a point to look at a MIT course on Neural Networks. And needless to say, I am getting to learn loads.
I would like to emphasize that though I have implemented a signature verification system using Neural Nets, I am by no means good with them. I can be classified a beginner. The tool that I am more comfortable with are Support Vector Machines.
I have been wanting to know more about them for some years now, but I never really got the time or you can say the opportunity. Now that I can invest some time, I am glad I came across this course. So far I have been able to look at 7 lectures and I should say that I am MORE than very happy with the course. I think it is very detailed and extremely well suited for the beginner as well as the expert.
The instructor is H. Sebastian Seung who is the professor of computational neuroscience at the MIT.
The course has 25 lectures each one packed with a great amount of information. Meaning, the lectures might work slow for those who are not very familiar with this stuff.
The video lectures can be accessed over here. I must admit that i am a little disappointed that these lectures are not available on you-tube. That’s because the downloads are rather large in size. But I found them worth it any way.
The lectures cover the following:
Lecture 1: Classical neurodynamics
Lecture 2: Linear threshold neuron
Lecture 3: Multilayer perceptrons
Lecture 4: Convolutional networks and vision
Lecture 5: Amplification and attenuation
Lecture 6: Lateral inhibition in the retina
Lecture 7: Linear recurrent networks
Lecture 8: Nonlinear global inhibition
Lecture 9: Permitted and forbidden sets
Lecture 10: Lateral excitation and inhibition
Lecture 11: Objectives and optimization
Lecture 12: Excitatory-inhibitory networks
Lecture 13: Associative memory I
Lecture 14: Associative memory II
Lecture 15: Vector quantization and competitive learning
Lecture 16: Principal component analysis
Lecture 17: Models of neural development
Lecture 18: Independent component analysis
Lecture 19: Nonnegative matrix factorization. Delta rule.
Lecture 20: Backpropagation I
Lecture 21: Backpropagation II
Lecture 22: Contrastive Hebbian learning
Lecture 23: Reinforcement Learning I
Lecture 24: Reinforcement Learning II
Lecture 25: Review session
The good thing is that I have formally studied most of the stuff after lecture 13 , but going by the quality of lectures so far (first 7), I would not mind seeing them again.
This is a Harvard course. I don’t know when I’ll get the time to have a look at this course, but it sure looks extremely interesting. And I am sure a number of people would be interested in having a look at it. It looks like a course that be covered up pretty quickly actually.
The course description says the following:
The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. The goal of this course is to expose you to visual representation methods and techniques that increase the understanding of complex data. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making.
In this course you will learn how the human visual system processes and perceives images, good design practices for visualization, tools for visualization of data from a variety of fields, collecting data from web sites with Python, and programming of interactive visualization applications using Processing.
The topics covered are:
- Data and Image Models
- Visual Perception & Cognitive Principles
- Color Encoding
- Design Principles of Effective Visualizations
- Graphs & Charts
- Trees and Networks
- Maps & Google Earth
- Higher-dimensional Data
- Unstructured Text and Document Collections
- Images and Video
- Scientific Visualization
- Medical Visualization
- Social Visualization
- Visualization & The Arts
Advanced AI Techniques:
This is one course that I would be looking at some parts of after I have covered the course on Neural Nets. I am yet to glance at the first lecture or the materials, so i can not say how they would be like. But I sure am expecting a lot from them going by the topics they are covering.
The topics covered in a broad sense are:
- Bayesian Networks
- Statistical NLP
- Reinforcement Learning
- Bayes Filtering
- Distributed AI and Multi-Agent systems
- An Introduction to Game Theory
I don’t know if I would be able to squeeze in time for these. But because of my amateurish interest in chemistry (If I were not an electrical engineer, I would have been into Chemistry), and because I have very high regard for Dr Harry Kroto (who is delivering them) I would try and make it a point to have a look at them. I think I’ll skip gym for some days to have a look at them. ;-)
[Nobel Laureate Harry Kroto with a Bucky-Ball model – Image Source : richarddawkins.net]