If you are inclined to make AI/DataScience your career, look no further. A clear-cut go-to path for the job that you aspire for. How to read a job description? How and where do you fit in? What are the courses (free / paid) that you should undertake to be successful.

Given the hype going on about data science, this is a very valid question, do you need a master's degree. If this hype is genuine or not is also another big question. But this article will focus on if a master's degree is necessary. There are so many other short, easier, and cheaper options out there. Is it still necessary to go through a big academic process?

Modern machine learning algorithms excel when the training and test distributions match but often fail under even moderate distribution shift (Beery et al., 2018); learning a predictor which generalizes to distributions which differ from the training data is therefore an important task. This objective, broadly referred to as out-of-distribution (OOD) generalization, is not realizable in general, so researchers have formalized several possible restrictions. Common choices include a structural assumption such as covariate or label shift (Widmer & Kubat, 1996; Bickel et al., 2009; Lipton et al., 2018) or expecting that the test distribution will lie in some uncertainty set around the training distribution (Bagnell, 2005; Rahimian & Mehrotra, 2019). One popular assumption is that the training data is comprised of a collection of "environments" (Blanchard et al., 2011; Muandet et al., 2013; Peters et al., 2016) or "groups" (Sagawa et al., 2020), each representing a distinct distribution, where the group identity of each sample is known. The hope is that by cleverly training on such a combination of groups, one can derive a robust predictor which will better transfer to unseen test data which relates to the observed distributions--such a task is known as domain generalization.

Learn to build Toxic Question Classifier engine with BERT and TensorFlow 2.4. A Powerful Skill at Your Fingertips Learning the fundamentals of text classification h puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, have excellent documentation. Text classification is a fundamental task in natural language processing (NLP) world. No prior knowledge of word embedding or BERT is assumed.

Motivated by online decision-making in time-varying combinatorial environments, we study the problem of transforming offline algorithms to their online counterparts. We focus on offline combinatorial problems that are amenable to a constant factor approximation using a greedy algorithm that is robust to local errors. For such problems, we provide a general framework that efficiently transforms offline robust greedy algorithms to online ones using Blackwell approachability. We show that the resulting online algorithms have $O(\sqrt{T})$ (approximate) regret under the full information setting. We further introduce a bandit extension of Blackwell approachability that we call Bandit Blackwell approachability. We leverage this notion to transform greedy robust offline algorithms into a $O(T^{2/3})$ (approximate) regret in the bandit setting. Demonstrating the flexibility of our framework, we apply our offline-to-online transformation to several problems at the intersection of revenue management, market design, and online optimization, including product ranking optimization in online platforms, reserve price optimization in auctions, and submodular maximization. We show that our transformation, when applied to these applications, leads to new regret bounds or improves the current known bounds.

We study online learning for optimal allocation when the resource to be allocated is time. Examples of possible applications include a driver filling a day with rides, a landlord renting an estate, etc. Following our initial motivation, a driver receives ride proposals sequentially according to a Poisson process and can either accept or reject a proposed ride. If she accepts the proposal, she is busy for the duration of the ride and obtains a reward that depends on the ride duration. If she rejects it, she remains on hold until a new ride proposal arrives. We study the regret incurred by the driver first when she knows her reward function but does not know the distribution of the ride duration, and then when she does not know her reward function, either. Faster rates are finally obtained by adding structural assumptions on the distribution of rides or on the reward function. This natural setting bears similarities with contextual (one-armed) bandits, but with the crucial difference that the normalized reward associated to a context depends on the whole distribution of contexts.

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We study the problem of Online Convex Optimization (OCO) with memory, which allows loss functions to depend on past decisions and thus captures temporal effects of learning problems. In this paper, we introduce dynamic policy regret as the performance measure to design algorithms robust to non-stationary environments, which competes algorithms' decisions with a sequence of changing comparators. We propose a novel algorithm for OCO with memory that provably enjoys an optimal dynamic policy regret. The key technical challenge is how to control the switching cost, the cumulative movements of player's decisions, which is neatly addressed by a novel decomposition of dynamic policy regret and an appropriate meta-expert structure. Furthermore, we generalize the results to the problem of online non-stochastic control, i.e., controlling a linear dynamical system with adversarial disturbance and convex loss functions. We derive a novel gradient-based controller with dynamic policy regret guarantees, which is the first controller competitive to a sequence of changing policies.

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We introduce the technique of generic chaining and majorizing measures for controlling sequential Rademacher complexity. We relate majorizing measures to the notion of fractional covering numbers, which we show to be dominated in terms of sequential scale-sensitive dimensions in a horizon-independent way, and, under additional complexity assumptions establish a tight control on worst-case sequential Rademacher complexity in terms of the integral of sequential scale-sensitive dimension. Finally, we establish a tight contraction inequality for worst-case sequential Rademacher complexity. The above constitutes the resolution of a number of outstanding open problems in extending the classical theory of empirical processes to the sequential case, and, in turn, establishes sharp results for online learning.