Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions (denoted as $\mu$ and $\mu'$, respectively). In this work, we give an information-theoretic analysis on the generalization error and the excess risk of transfer learning algorithms, following a line of work initiated by Russo and Zhou. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence $D(mu||mu')$ plays an important role in characterizing the generalization error in the settings of domain adaptation. Specifically, we provide generalization error upper bounds for general transfer learning algorithms and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase. We further apply the method to iterative, noisy gradient descent algorithms, and obtain upper bounds which can be easily calculated, only using parameters from the learning algorithms. A few illustrative examples are provided to demonstrate the usefulness of the results. In particular, our bound is tighter in specific classification problems than the bound derived using Rademacher complexity.

The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a key task in the development of an intelligent tutoring system. In this paper we propose a novel approach to knowledge tracing that combines techniques from matrix factorization with recent progress in recurrent neural networks (RNNs) to effectively track the state of a student's knowledge. The proposed \emph{DynEmb} framework enables the tracking of student knowledge even without the concept/skill tag information that other knowledge tracing models require while simultaneously achieving superior performance. We provide experimental evaluations demonstrating that DynEmb achieves improved performance compared to baselines and illustrating the robustness and effectiveness of the proposed framework. We also evaluate our approach using several real-world datasets showing that the proposed model outperforms the previous state-of-the-art. These results suggest that combining embedding models with sequential models such as RNNs is a promising new direction for knowledge tracing.

Numerical reasoning is often important to accurately understand the world. Recently, several format-specific datasets have been proposed, such as numerical reasoning in the settings of Natural Language Inference (NLI), Reading Comprehension (RC), and Question Answering (QA). Several format-specific models and architectures in response to those datasets have also been proposed. However, there exists a strong need for a benchmark which can evaluate the abilities of models, in performing question format independent numerical reasoning, as (i) the numerical reasoning capabilities we want to teach are not controlled by question formats, (ii) for numerical reasoning technology to have the best possible application, it must be able to process language and reason in a way that is not exclusive to a single format, task, dataset or domain. In pursuit of this goal, we introduce NUMBERGAME, a multifaceted benchmark to evaluate model performance across numerical reasoning tasks of eight diverse formats. We add four existing question types in our compilation. Two of the new types we add are about questions that require external numerical knowledge, commonsense knowledge and domain knowledge. For building a more practical numerical reasoning system, NUMBERGAME demands four capabilities beyond numerical reasoning: (i) detecting question format directly from data (ii) finding intermediate common format to which every format can be converted (iii) incorporating commonsense knowledge (iv) handling data imbalance across formats. We build several baselines, including a new model based on knowledge hunting using a cheatsheet. However, all baselines perform poorly in contrast to the human baselines, indicating the hardness of our benchmark. Our work takes forward the recent progress in generic system development, demonstrating the scope of these under-explored tasks.

During this surreal time, we found ourselves with some more time in our hands if some of us are placed into furlough, or simply working from home, we can use this new time, that I will call now QuaranTIME for this article, to learn new skills and to improve our existing skills. So I have decided to help out anyone interested in learning about Artificial Intelligence, Machine Learning, and Data Science, in general, some of the best resources I found helpful in my journey on these topics. Learning a new skill, concept or subject is not at all easy, and requires some discipline to make sure there is progress, a lot of people hit that brick wall when things start to get a bit more complex and tend to give up, usually due to time pressure, work creeping up into personal time, or responsibilities that take all of their time. So my advice is, have the time allocated for learning, at least one hour a day, maybe more if you can, split your quaranTIME in a smart way and you will see results quickly, personally I find that early morning, while I am having my coffee, is when I am the most receptive to new ideas, concepts, and the house is silent and still…. You will see there are an enormous amount of learning resources to choose from, books, courses, videos, etc.

Udemy Course Computer Science 101: Intro to Java & Algorithms NED Computer Science 101: Intro to Java & Algorithms by Tristan Hull, Joshua Benz 11 hours on-demand video Master Coding The Right Way! Learn Java and Algorithms with instructors Tristan and Joshua by Tristan Hull, Joshua Benz hat you'll learn Fundamentals of Programming Object Oriented Programming Basic Syntax to Expressions Selection Statements to Loops Advanced OOP Concepts Description Learn Java and Algorithms with instructors Tristan and Joshua. This course is designed for students who are struggling in their computer science program, or anyone that wants to learn programming with little to no prior experience. We will take you from level zero to mastery in no time. The two instructors have combined 20 years experience with software development and computer science. We designed this course to make sure the student actually understands, and to cover what every introduction college class would teach.

Limarc is a Tokyo-based writer of all things pop culture, travel, and tech. Subscribe to get your daily round-up of top tech stories!

Transformational advancements in technology in today's world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today's computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it! With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

Machine learning algorithms are increasingly being used to make decisions about the individuals in various areas such as university admissions, employment, health, etc. As the individuals gain information about the algorithms being used, they have an incentive to adapt their data so as to be classified desirably. For example, if a student is aware that a university heavily weighs SAT score in their admission process, she will be motivated to achieve a higher SAT score either through extensive test preparation or multiple tries. Such efforts by the students might not change their probability of being successful at the university, but are enough to fool the admissions' process. Therefore, under such "strategic manipulation" of one's data, the predictive power of the decisions are bound to decrease. One way to prevent such manipulation is by keeping the classification algorithms a secret, but this is not a practical solution to the problem, as some information is bound to leak over time and the transparency of these algorithms is a growing social concern. Thus, this motivates the study of algorithms that are optimal under "strategic manipulation". The problem of gaming in the context of classification algorithms is a well known problem and is increasingly gaining researchers' attention, for example, [HMPW16, ALB16, HIV19, MMDH19, DRS

It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different `mechanism', hitherto not articulated in the literature, may also be responsible for machine's bias, namely that biases may originate from (i) the programmers' cultural background, such as education or line of work, or (ii) the contextual programming environment, such as software requirements or developer tools. Combining an experimental and comparative design, we studied the effects of cultural metaphors and contextual metaphors, and tested whether each of these would `transfer' from the programmer to program, thus constituting a machine bias. The results show (i) that cultural metaphors influence the programmer's choices and (ii) that `induced' contextual metaphors can be used to moderate or exacerbate the effects of the cultural metaphors. This supports our hypothesis that biases in automated systems do not always originate from within the machine's training data. Instead, machines may also `replicate' and `reproduce' biases from the programmers' cultural background by the transfer of cultural metaphors into the programming process. Implications for academia and professional practice range from the micro programming-level to the macro national-regulations or educational level, and span across all societal domains where software-based systems are operating such as the popular AI-based automated decision support systems.

By now you have learned the basics of machine learning and a bit of Python 3 and Pandas. Here are a few next steps, and free resources to get you going. I will keep adding information here as I think of it, or from suggestions in the comments. At this point, you should not read the documentation as if were a book (although you can do so if this works for you). Browse the documentation top-down to familiarize yourself with the various topics available.