About this course: This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you're struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track.

About this course: This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the "rise over run" formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be.

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During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

Similarity/Distance measures play a key role in many machine learning, pattern recognition, and data mining algorithms, which leads to the emergence of metric learning field. Many metric learning algorithms learn a global distance function from data that satisfy the constraints of the problem. However, in many real-world datasets that the discrimination power of features varies in the different regions of input space, a global metric is often unable to capture the complexity of the task. To address this challenge, local metric learning methods are proposed that learn multiple metrics across the different regions of input space. Some advantages of these methods are high flexibility and the ability to learn a nonlinear mapping but typically achieves at the expense of higher time requirement and overfitting problem. To overcome these challenges, this research presents an online multiple metric learning framework. Each metric in the proposed framework is composed of a global and a local component learned simultaneously. Adding a global component to a local metric efficiently reduce the problem of overfitting. The proposed framework is also scalable with both sample size and the dimension of input data. To the best of our knowledge, this is the first local online similarity/distance learning framework based on PA (Passive/Aggressive). In addition, for scalability with the dimension of input data, DRP (Dual Random Projection) is extended for local online learning in the present work. It enables our methods to be run efficiently on high-dimensional datasets, while maintains their predictive performance. The proposed framework provides a straightforward local extension to any global online similarity/distance learning algorithm based on PA.

As a software engineer at Microsoft, Elena Voyloshnikova's job is to make informed recommendations about how to improve the performance of software engineering tools. But too often, she spends her days manually analyzing the data she needs to make those decisions. Lately, her team has been discussing the potential of building machine learning models to automate that task – creating more time to focus on the decision-making. That's why she was intrigued when she received an email announcing an upcoming AI training session for Microsoft employees. "I asked my manager, 'Can I go to this?'" she said.

More companies are buying into the hype of artificial intelligence every day, though reaping the rewards can be a tricky game. After all, it is more often than not that the practical, not the sexy, AI application boosts a company's bottom-line. Balancing the influx of new information, from apocalyptic musings on the future of the technology to highly technical research reports -- incomprehensible to this writer and many of the most eager computer scientists -- is a challenge, but that's a problem Twitter can actually help with. Position: Co-founder at Coursera; adjunct professor at Stanford University; board of directors at drive.ai; chairman of the board at Woebot Ng is a staple in the AI sphere and helped build up some of the most prominent AI insititutions today, such as Google Brain and Baidu. He is also a driving force in AI education, and through his online learning program Coursera, Ng is trying to teach millions of new AI experts.

We derive upper and lower bounds for the policy regret of $T$-round online learning problems with graph-structured feedback, where the adversary is nonoblivious but assumed to have a bounded memory. We obtain upper bounds of $\widetilde O(T^{2/3})$ and $\widetilde O(T^{3/4})$ for strongly-observable and weakly-observable graphs, respectively, based on analyzing a variant of the Exp3 algorithm. When the adversary is allowed a bounded memory of size 1, we show that a matching lower bound of $\widetilde\Omega(T^{2/3})$ is achieved in the case of full-information feedback. We also study the particular loss structure of an oblivious adversary with switching costs, and show that in such a setting, non-revealing strongly-observable feedback graphs achieve a lower bound of $\widetilde\Omega(T^{2/3})$, as well.

This class is a great overview of the types of machine-learning models, and some of the history of how those models came into use. The fundamental explanations of complex ideas are generally excellent and very clear, but the practical equations that are necessary for implementations are difficult to understand for someone like me who is not used to reading abstract mathematical equations. Examples of equations that are worked out with explicit values are few and far between and this doesn't help. This makes the programming assignments exceptionally difficult even though the code they require is simple. Also, the amount of time required for this course is enormous, easily 10x what is predicted when there is a programming assignment.

Employees at all stages of their careers are challenged by the technological and socio-economical changes that are limiting the suitability of these employee's current skills and learning. Widening gap between the skills available and skills in demand is certainly alarming and you should not overlook a timely career advice. To brace yourself for a future-ready career you will require advanced technical training or specialized education. Dynamic re-skilling and learning on-the-go are keys to be successful in the competitive job market. Everybody is talking about Artificial Intelligence.