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Last week, World Economic Forum released a paper on how to prevent discrimination of humans in machine learning? In which it provides a framework for developers to understand the potential risks associated with machine learning applications and how to combat marginalisation and discrimination of humans in AI and encourage dignity assurance. And focus on how companies designing and implementing machine learning technology can maximize its potential benefits. It also offers a set of transferable, guiding principles for the field of machine learning.
Tech giants like Google, Microsoft and Deepmind (Alphabet) have begun to explore the ideas of fairness, inclusion, accountability and transparency in machine learning. However, with AI continuing to influence more people in employment, education, healthcare etc and mostly in the absence of adequate government regulation – whether due to technology outpacing regulatory mechanisms, lack of government capacity, political turmoil – there is a need to more active self-governance by private companies, highlights the WEF paper.

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Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea (at least for “supervised learning,” by far the most common type in business) is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don’t know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models.
One company, however, is rapidly developing a “machine learning machine” that can build and deploy very large numbers of models with relatively little human intervention. You may have heard of dunnhumby, a UK-based analytics company that’s owned by the big retailer Tesco. dunnhumby had a US joint venture with Kroger named dunnhumbyUSA. In 2015 Kroger purchased dunnhumbyUSA and named it 84.51°. The name coincides with the location of its Cincinnati headquarters and is a tribute to the longitudinal analytics the company employs. But 84.51° also analyzes data like a finely-tuned machine.

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Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea (at least for “supervised learning,” by far the most common type in business) is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don’t know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models.
One company, however, is rapidly developing a “machine learning machine” that can build and deploy very large numbers of models with relatively little human intervention. You may have heard of dunnhumby, a UK-based analytics company that’s owned by the big retailer Tesco. dunnhumby had a US joint venture with Kroger named dunnhumbyUSA. In 2015 Kroger purchased dunnhumbyUSA and named it 84.51°. The name coincides with the location of its Cincinnati headquarters and is a tribute to the longitudinal analytics the company employs. But 84.51° also analyzes data like a finely-tuned machine.

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Machine learning is changing the way we do things, and it’s becoming mainstream very quickly.
Related: 5 Strategies From Top Firms on How to Use Machine Learning
While many factors have contributed to this increase in machine learning, one reason is that it’s becoming easier for developers to apply it, thanks to open source frameworks.
If you’re not familiar with this technology, and feel confused about some of the terms used, such as “framework” and “library,” here are the definitions:
Framework. A vague term, to be sure; even those who regularly use it can’t agree on its exact definition. However, in most cases, "framework" refers to a bunch of programs, libraries and languages you have built to use in application development. Think of a framework as a base for getting started.
Library. A collection of objects or methods that your application uses. It’s a file with re-usable code that can be shared by many applications, so you don’t have to write the same code repeatedly. Instead, you link to the library.
As one online user put it: “The key difference between a library and a framework is 'inversion of control.' When you call a method from a library, you are in control. But with a framework, the control is inverted: The framework calls you.”
Still confused? Check out this helpful YouTube video about the difference between a framework and a library.
If you’re diving into machine learning in a big way, you’re probably seeking resources to help guide you. There are many frameworks available, but here are some of our favorites to help you get started.

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Looks like it’s humans: 0, computers: 1 again. If you’re a big enough fan of ramen, maybe you can look at a photo of a tonkotsu bowl on Instagram and immediately recognize which restaurant it’s from. But computers have us beat, as they can now identify the exact shop a menu item came from, out of 41 seemingly identical bowls of ramen from the same restaurant franchise.
Data scientist Kenji Doi did the delicious research, using Google’s AutoML Vision to classify every menu item from Ramen Jiro, a Tokyo-based chain of ramen shops. He gathered about 1,170 photos from each of the 41 shops, and fed the dataset of 48,000 ramen photos to the software. It took AutoML about 24 hours (18 minutes, in a less accurate Basic mode) to finish training the data, and the model was able to predict which shop the ramen came from with a 95 percent accuracy.

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The growth of the Internet of Things (IoT) market in recent years is hard to ignore. According to Forbes, the global IoT market will grow from $157 billion to $457 billion between the year 2016 and 2020. The major contributors to the investment include leading industries like manufacturing, logistics, and transportation.
When it comes to sectors that dominate this investment, smart city initiatives and industrial IoT top the chart by owning more than 50 percent of the market. Gartner predicts that more than 65 percent of enterprises will adopt IoT products by the year 2020.
A typical IoT solution pipeline consists of the following five
The heart of this process and what drives the real business value is encapsulated in the third stage of this activity chain, which is 'Transformation and Analytics'. This is the stage where the data is inspected and decisions are made. These decisions will directly influence the actions that will optimise business flows.
This is where the role of machine learning and artificial intelligence becomes significant. The ability of the system to make cognitive decisions based on historical data will greatly influence the value of the solution. Technologies like Azure Machine learning can leverage supervised learning techniques to help make business decisions based on classification, regression, and anomaly detection.
Machine learning - evolution
The concept of machine learning is not new to the world of computing. The birth of the term happened in the late 1950s, inspired from related fields in computing such as pattern recognition and artificial intelligence. However, leveraging this concept to optimise business process was largely constrained by the cost of provisioning and maintaining the compute and storage required to host and execute machine learning algorithms.
The primary cause for the re-emergence of machine learning is the evolution of cloud computing and its adoption in today's enterprise world. By offering features like infinitely scalable compute and storage, high-performance computing services, and pay-per-use subscription model, cloud computing became the ideal surrogate to bring machine learning back to life. This enabled organisations of any scale to affordably run machine learning algorithms to optimise their business processes. It also encouraged cloud market giants like Microsoft, Amazon, and Google to offer this technology as a software service consumable on a subscription model.
Machine Learning and IoT
Machine learning uses supervised learning techniques on historical data to make cognitive decisions. The greater the quantity of historic data, the better the decision-making capabilities of the algorithm. This philosophy makes IoT the ideal use case for machine learning as the data generated by the devices are usually very frequent.
The following are few common scenarios where machine learning works hand-in-hand with IoT to enable business optimisations:
Anomaly monitoring — Azure machine learning can be used to detect anomalies in time series data, in data feeds sent by the IoT devices that are uniformly spaced in time. Anomalies like spikes and dips, positive and negative trends, can be detected using a machine learning algorithm monitoring the live stream of device feeds.
Predictive maintenance — Predictive maintenance directly impacts the costs for an organisation, which makes it one of the most popular machine learning solutions. The ability of machine learning algorithms to foresee possibilities of a device failing, remaining life of an equipment, and causes of failure can enable the business to optimise operational cost by reducing the maintenance time significantly.
Vehicle telemetry — The capability of machine learning solutions to ingest millions of events from vehicles to improve their safety, reliability, and driving experience makes it a desirable technology to adopt for transportation and logistics industries.

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While machines have simplified our lives and relieved us of certain redundant activities, there is cause to worry if we have created a monster out of the machines.
Technology has advanced so much that what was before only seen in sci-fi movies are now all around us. Take, for example, the 100,000 individual IoT sensors that stud the 1400-kilometre waterway that connects the Danjiangkou Reservoir to Beijing and Tianjin, to monitor structural damage, tracking water quality and flow rates, and watching for intruders, whether humans or animals. Or, KIT's ARMAR-6 Humanoid, which helps humans fix other robots, or even a no steering wheel GM car. Media coverage, today, is liberally sprinkled with the following terms - 5G, Internet of Things, artificial intelligence/machine learning, robots, and driverless cars, and for good reasons. The time is now.
When we look at the recently concluded CES 2018, the world's gathering place for all those who thrive on the business of consumer technologies, we see a common theme – pushing boundaries of technology.

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Machine learning studies automatic methods for identifying patterns in complex data and for making accurate predictions based on past observations. In this course, we develop rigorous mathematical foundations of machine learning, in order to provide guarantees about the behaviour of learning algorithms and also to understand the inherent difficulty of learning problems.
The course will begin by providing a statistical and computational toolkit, such as generalisation guarantees, fundamental algorithms, and methods to analyse learning algorithms. We will cover questions such as when can we generalise well from limited amounts of data, how can we develop algorithms that are computationally efficient, and understand statistical and computational trade-offs in learning algorithms. We will also discuss new models designed to address relevant practical questions of the day, such as learning with limited memory, communication, privacy, and labelled and unlabelled data. In addition to core concepts from machine learning, we will make connections to principal ideas from information theory, game theory and optimisation.
This is an advanced course requiring a high level of mathematical maturity. It is expected that students will have taken prior courses on machine learning, algorithms, and complexity.
NB. There is no past paper for the Advanced Machine Learning course. Whilst you may refer to the Computational Theory exam in preparation, please note that the content and scope of the version will be different.

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Advanced Machine Learning by davefollmersdavefollmers, 13 Mar 2018 06:19

earning to recognize and categorize objects is an essential cognitive skill allowing animals to function in the world. For example, recognizing another animal as a friend or a foe allows for determining how to interact with it. Likewise, recognizing a plant as edible (or not) can ensure survival. However, animals do not often have access to an ideal view of an object if it is separated from its environment. The same object is often seen with a different viewpoint, partially obstructed, or in less than ideal lighting conditions. Therefore, it is essential to study categorization under noisy and degraded conditions.
How does the brain process categorization stimuli in degraded conditions? One possibility is that brain areas typically associated with visual processing in posterior cortex (e.g., V1, V2, V3, V4) extract the stimulus from its environment (background noise), and that brain areas typically associated with categorization [e.g., striatum, prefrontal cortex (PFC), hippocampus (HC)] are not affected by the degraded conditions. Another possibility is that visual processing is not affected by the viewing condition, but that the categorization systems receive a degraded stimulus representation in poor viewing conditions and need to adjust their processing accordingly.
To disentangle these two possibilities young adults were scanned at the Purdue MRI Facility while categorizing novel abstract stimuli that were covered by masks with different levels of transparencies. Advanced machine learning methods were used to process the brain activity and try to predict the viewing conditions of the stimuli based only on the measured brain activity. This process is sometimes referred as “mind reading” and uses a support vector machine (SVM). The results of full brain analysis showed that the SVM could discriminate between the most degraded visual condition and the other two (less degraded) viewing conditions.
Analysis of the patterns learned by the SVM shows that posterior visual areas V1, V2, V3, and V4 were the most important in discriminating between the different viewing conditions. This result was further supported by region of interest (ROI) analyses that focus on specific brain areas. The ROI analyses showed that the activity in brain areas V1, V2, V3, and V4 were each individually capable of identifying the level of stimulus degradation. In contrast, the striatum, PFC, and HC, brain areas generally associated with stimulus categorization, were unable to identify the level of stimulus degradation.

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Hello,
I cannot access my banking information through Mint or my bank's budgeting program. I had to deactivate in Mint because its repeated attempts to access was causing a block on my account. I can't monitor my expenses/budgeting any more. Right now both the bank (an international one with four initials, the last being "C") and Mint are "working on it". Is there a finance management program that allows downloading of bank/CC info, but would accommodate a different sign-in each time? Any other suggestions?
Thanks
Please help

I didn't find the right solution from the Internet
References:
https://social.msdn.microsoft.com/Forums=5365754/pg/windows/en
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stud (guest) 13 Oct 2017 15:10
in discussion News / Course News » Final Grades

Hi,
Is a grading table for the project will be published?
Specifically, how the different grades scaling between projects that are merely for this course and for project combined with the NLP course was preformed?
thanks

by stud (guest), 13 Oct 2017 15:10
Final Grades
Daniel CarmonDaniel Carmon 04 Oct 2017 08:58
in discussion News / Course News » Final Grades

Hi,
Since the uni offices are closed now, and we already have the final grades ready, we've decided to publish them on the course website.
The grade table is now available at the bottom of the "General Information" page.

If you have any questions regarding the h.w grading, feel free to mail me at: li.ca.uat.liam|adnomrac#li.ca.uat.liam|adnomrac.
Happy holidays,
Daniel.

Final Grades by Daniel CarmonDaniel Carmon, 04 Oct 2017 08:58
Stu (guest) 03 Oct 2017 18:10
in discussion News / Course News » Course grade

Can you please publish here the final corrected grades? The university offices are closed for a long period. Thanks!

by Stu (guest), 03 Oct 2017 18:10
Course grade
Amir Globerson (guest) 02 Oct 2017 18:25
in discussion News / Course News » Course grade

There was a problem with the reported grades. Everyone please ignore your grades until it is updated.
Sorry about this.
Amir

Course grade by Amir Globerson (guest), 02 Oct 2017 18:25
Daniel (guest) 25 Sep 2017 13:18
in discussion Discussions / HW4 » HW Grades

Hi,
The graded hw are still at my mailbox (no. 263), at the first floor of the Schreiber building.

Daniel

by Daniel (guest), 25 Sep 2017 13:18
Gal (guest) 17 Sep 2017 07:21
in discussion Discussions / HW4 » HW Grades

Hi,
Where are the graded HW now?
thanks

by Gal (guest), 17 Sep 2017 07:21
Daniel CarmonDaniel Carmon 09 Jul 2017 14:40
in discussion Discussions / HW4 » HW Grades

Thanks, fixed it.

Daniel

by Daniel CarmonDaniel Carmon, 09 Jul 2017 14:40
Roey (guest) 09 Jul 2017 05:33
in discussion Discussions / HW4 » HW Grades

The file doesn't exist.

by Roey (guest), 09 Jul 2017 05:33
HW Grades
Daniel CarmonDaniel Carmon 07 Jul 2017 14:57
in discussion Discussions / HW4 » HW Grades

Hi,

I uploaded a table with the grades for HW 1-4.
For HW4, some students didn't seem to submit the theory questions / the programming assignment.
If you believe you submitted the theory questions / programming assignment but the table says otherwise,
please mail me at:
li.ca.uat.liam|adnomrac#li.ca.uat.liam|adnomrac

Thanks,
Daniel

HW Grades by Daniel CarmonDaniel Carmon, 07 Jul 2017 14:57
HW Grades
Daniel CarmonDaniel Carmon 04 Jul 2017 19:13
in discussion Discussions / General » HW Grades

Hi,
A sheet with grades for HW 1-3 is now in the Home Assignments section.
The grades for HW4 will be there soon.
In case you think there's a mistake (e.g you submitted exercise $k\in\{1,2,3\}$ and the table says you didn't), please mail me at:
li.ca.uat.liam|adnomrac#li.ca.uat.liam|adnomrac

Thanks,
Daniel

HW Grades by Daniel CarmonDaniel Carmon, 04 Jul 2017 19:13
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