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This page continas all my coursera machine learning courses and resources by Prof. Andrew Ng. The most of the course talking about hypothesis function and minimising cost funtions. A hypothesis is a certain function that we believe or hope is similar to the true function, the target function that we want to model.

In context of email spam classification, it would be the rule we came up with that allows us to separate spam from non-spam emails. The cost function or Sum of Squeared Errors SSE is a measure of how far away our hypothesis is from the optimal hypothesis. The closer our hypothesis matches the training examples, the smaller the value of the cost function.

Gradient descent is an iterative minimization method. The gradient of the error function always shows in the direction of the steepest ascent of the error function. Thus, we can start with a random weight vector and subsequently follow the negative gradient using a learning rate alpha.

Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting.This book provides a single source introduction to the field. It is written for advanced undergraduate and graduate students, and for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed. Important Notes :. It is a collection of lectures notes not ours. Our subjective is to help students to find all engineering notes with different lectures PowerPoint slides in ppt ,pdf or html file at one place.

Because we always face that we lose much time by searching in Google or yahoo like search engines to find or downloading a good lecture notes in our subject area with free. Also it is difficult to find popular authoress or books slides with free of cost. If you find any copyrighted slides or notes then please inform me immediately by comments or email as following address. I will take actions to remove it.

I will must consider your comments only within days. To find your notes quickly please see the contents on the right hand side of this page which is alphabetically arranged and right click on it.

It is better to search your subject notes by clicking on search button which is present at middle of right side of this web page. Then enter your subject and press enter key then you can find all of your lectures notes and click on it. Thank you for visiting our site Click Below to Download the files Slides are available in both postscript, and in latex source.

If you take the latex, be sure to also take the accomanying style files, postscript figures, etc. Very Useful and thought provoking book for Machine Learning Reserch aspirants. Research Scholar,Krishna University,s.

Artificial Intelligence Lecture Materials

Pages Home Contact. Search This Blog. Machine Learning is the study of computer algorithms that improve automatically through experience. Applications range from datamining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests.Time and Location : Monday, Wednesday pmpm, links to lecture are on Canvas.

Note : This is being updated for Spring The dates are subject to change as we figure out deadlines.

Please check back soon. Linear Regression. Logistic Regression. Netwon's Method Perceptron. Exponential Family. Generalized Linear Models. Naive Bayes. Laplace Smoothing. Support Vector Machines. GMM non EM. Expectation Maximization. Factor Analysis. Value function approximation. Previous projects: A list of last quarter's final projects can be found here. Data: Here is the UCI Machine learning repositorywhich contains a large collection of standard datasets for testing learning algorithms.

Slides Introduction slides [ pptx ] Introduction slides [ pdf ].

An Introduction To Machine Learning Hindi

Problem Set 0. Weighted Least Squares. Class Notes [ live lecture notes ]. Problem Set 1.

Coursera Machine Learning

Class Notes Generative Algorithms. Class Notes Support Vector Machines. Class Notes Deep Learning Backpropagation. Problem Set 2.

machine learning lecture notes ppt

Notes Evaluation Metrics. Class Notes Regularization and Model Selection. Notes Deep Learning. Problem Set 3. Class Notes Midterm review. Class Notes Factor Analysis. Class Notes Decision trees Decision tree ipython demo Boosting algorithms and weak learning. Class Notes Weak Supervision. Problem Set 4. Class Notes On critiques of ML. Class Notes Reinforcement Learning and Control. Other Resources Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here.Machine learning aims to build computer systems that learn from experience or data.

Instead of being programmed by humans to follow the rules of human experts, learning systems develop their own rules from trial-and-error experience to solve problems. Machine learning is an exciting interdisciplinary field with roots in computer science, pattern recognition, mathematics and even neuroscience.

The field is experiencing rapid development and has found numerous exciting real-world applications. This course gives an introduction to the principles, techniques and applications of machine learning.

Topics covered include. Main Textbook Tom M. Han and M. I have divided my slides into distinctive topics, sometimes these will be presented within one lecture, on other occasions they will be spread across multiple lectures. Slides and supporting materials will appear at least one week in advance of presentation during the course. Slides and handouts are no replacement of textbooks, you are expected to study recommended reading materials and do the exercise questions.

Short Notes 01 Slides. Topic 2 —Artificial neural networks. Short Notes 02 Slides, perceptron. Topic 3 — Bayesian learning. Topic 4 — Instance based learning.

Machine Learning - ML Study Materials

Slides, K-NN. Readings - Chapter 8 of Mitchell Exercises - see end of above slides. Topic 5 — Clustering analysis.

machine learning lecture notes ppt

Exercises - see end of above slides. Topic 6 — Data processing and representations. Readings - A tutorial on PCA by Jonathon Schlensalso see relevant chapters of the above references Exercises : see examples in the slides.

Topic 9 — Summary. Haykin, Neural Networks - A Comprehensive Foundation, Prentic-Hall, Slides and Handouts I have divided my slides into distinctive topics, sometimes these will be presented within one lecture, on other occasions they will be spread across multiple lectures. Topic 1 — Introduction and review of basic maths Short Notes 01 Slides. Exercises - see end of above slides Topic 6 — Data processing and representations Slides.Download the video from iTunes U or the Internet Archive.

Description: In this lecture, Prof. Guttag introduces machine learning and shows examples of supervised learning using feature vectors.

CSC2515 Fall 2008 - Lectures

The following content is provided under a Creative Commons license. Welcome back. You know, it's that time a term when we're all kind of doing this. So let me see if I can get a few smiles by simply noting to you that two weeks from today is the last class.

Should be worth at least a little bit of a smile, right? Professor Guttag is smiling. He likes that idea. You're almost there. What are we doing for the last couple of lectures? We're talking about linear regression. And I just want to remind you, this was the idea of I have some experimental data. Case of a spring where I put different weights on measure displacements. And regression was giving us a way of deducing a model to fit that data. And In some cases it was easy. We knew, for example, it was going to be a linear model.

We found the best line that would fit that data. In some cases, we said we could use validation to actually let us explore to find the best model that would fit it, whether a linear, a quadratic, a cubic, some higher order thing. So we'll be using that to deduce something about a model. That's a nice segue into the topic for the next three lectures, the last big topic of the class, which is machine learning. And I'm going to argue, you can debate whether that's actually an example of learning.

But it has many of the elements that we want to talk about when we talk about machine learning. So as always, there's a reading assignment. Chapter 22 of the book gives you a good start on this, and it will follow up with other pieces. And I want to start by basically outlining what we're going to do. And I'm going to begin by saying, as I'm sure you're aware, this is a huge topic.

I've listed just five subjects in course six that all focus on machine learning. And that doesn't include other subjects where learning is a central part. So natural language processing, computational biology, computer vision robotics all rely today, heavily on machine learning.

And you'll see those in those subjects as well. So we're not going to compress five subjects into three lectures. But what we are going to do is give you the introduction. We're going to start by talking about the basic concepts of machine learning.

The idea of having examples, and how do you talk about features representing those examples, how do you measure distances between them, and use the notion of distance to try and group similar things together as a way of doing machine learning.Previous material.

This is a tentative schedule and is subject to change. Please note that Youtube takes some time to process videos before they become available. Machine learning examples Well defined machine learning problem Decision tree learning. Mitchell: Ch 3 Bishop: Ch Slides Video. The big picture Overfitting Random variables and probabilities. Slides Annotated Slides Video. Mitchell: Naive Bayes and Logistic Regression. Gaussian Bayes classifiers Document Classification Brain image classification Form of decision surfaces.

Rademacher Complexity Overfitting and Regularization. Bayes Nets Representing joint distributions with conditional independence assumptions. Inference Learning from fully observed data Learning from partially observed data. Annotated Slides Video.

machine learning lecture notes ppt

EM Semi-supervised learning. Mixture of Gaussian clustering K-means clustering. Adaboost: Generalization Guarantees naive and margins based.

machine learning lecture notes ppt

Geometric Margins and Perceptron. Partitional Clustering Hierarchical Clustering. Hastie, Tibshirani and Friedman, Chapter Learning Representations Dimensionality Reduction.

Mitchell, Chapter 13 Kaelbling, et al.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The most of the course talking about hypothesis function and minimising cost funtions. A hypothesis is a certain function that we believe or hope is similar to the true function, the target function that we want to model.

In context of email spam classification, it would be the rule we came up with that allows us to separate spam from non-spam emails. The cost function or Sum of Squeared Errors SSE is a measure of how far away our hypothesis is from the optimal hypothesis. The closer our hypothesis matches the training examples, the smaller the value of the cost function.

Gradient descent is an iterative minimization method. The gradient of the error function always shows in the direction of the steepest ascent of the error function. Thus, we can start with a random weight vector and subsequently follow the negative gradient using a learning rate alpha.

When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". There is a tradeoff between a model's ability to minimize bias and variance. Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting.

Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Coursera Machine Learning By Prof. Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit b11d Aug 28, Machine Learning By Prof. When will the deep learning bubble burst?

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Sep 14, Update references to slides. Aug 28,