research papers on machine learning for beginners

Beginning with machine learning: a comprehensive primer

• Published: 21 July 2021
• Volume 230 , pages 2363–2444, ( 2021 )

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• Rahul Yedida 1 &
• Snehanshu Saha 2  

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This is a primer on machine learning for beginners. Certainly, there are plenty of excellent books on the subject, providing detailed explanations of many algorithms. The intent of this primer is not to outdo these texts in rigor; rather, to provide an introduction to the subject that is accessible, yet covers all the mathematical details, and provides implementations of most algorithms in Python. We feel this provides a well-rounded understanding of each algorithm: only by writing the code and seeing the math applied, and visually inspecting the algorithm’s working, will a reader be fully able to connect all the dots. The style of the primer is largely conversational, and avoids too much formal jargon. We will certainly introduce all required technical terms, but while explaining an algorithm, we will use simple English and avoid unnecessarily formalisms. We hope this proves useful for individuals willing to seriously study the subject.

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This manuscript has associated data in a data repository. [Authors’ comment: ...].

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Rahul Yedida

CSIS and APPCAIR, BITS Pilani K K Birla Goa Campus, Sancoale, India

Snehanshu Saha

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Yedida, R., Saha, S. Beginning with machine learning: a comprehensive primer. Eur. Phys. J. Spec. Top. 230 , 2363–2444 (2021). https://doi.org/10.1140/epjs/s11734-021-00209-7

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Received : 14 November 2020

Accepted : 22 June 2021

Published : 21 July 2021

Issue Date : September 2021

DOI : https://doi.org/10.1140/epjs/s11734-021-00209-7

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Table of Contents

Getting Started with Research Papers on Machine Learning: What to Read & How

A quick glance into any of the top-rated research papers on Machine Learning shows us how Machine Learning and digital technologies are becoming an integral part of every industry.

According to recent research by Gartner, “Smart machines will enter mainstream adoption by 2021.” Adopting Machine Learning help your organization gain a major competitive edge.

Why is Machine Learning so Hot Today?

With over 250 million active customers and tens of millions of products, Amazon’s machine learning makes accurate product recommendations. These recommendations are an outcome of the customer’s browsing and purchasing behavior almost instantly. No humans could do that.

Google is using driverless cars with the help of machine learning to make our roads safer. IBM’s Watson is already a big name in healthcare with its machine learning and cognitive computing power.

If you have an interest in a career in Machine Learning or Deep Learning, you must develop a habit of reading Research Papers on Machine Learning regularly. Reading research papers in Machine Learning keeps you abreast of the latest trends and thoughts.

The course books define the basic premises of your learning Research papers on Machine Learning give you a deeper understanding of the implementation models in every industry.

Being an ML professional your primary task is to think about problems that are difficult to identify. Solve them through innovative means, rather than memorize what has already been found.

Another advantage of browsing through research papers on machine learning is that you can learn Machine Learning algorithms better. Students or ML professionals who read research papers on machine learning algorithms have a better understanding of programming and coding.

Want to Know How Machine Learning Is Impacting our Lives?

The food or grocery segment is one area where Machine Learning has left an indelible mark.  Up to 40% of a grocer’s revenue comes from sales of fresh produce. Therefore, maintaining product quality is very important. But that is easier said than done.

Grocers are dependent on their supply chains and consumers. Keeping their shelves stocked and their products fresh is a difficult situation for them.

But with machine learning grocers already know the secret to smarter fresh-food replenishment. They can train ML programs on historical datasets and input data about promotions and store hours as well. Then use the analyses to gauge how much of each product to order and display.

ML systems can also collect information about weather forecasts, public holidays, order quantity parameters, and other contextual information.

Grocers or store-owners can then issue a recommended order every 24 hours so that the grocer always has the appropriate products in the appropriate amounts in stock.

Research Papers on Machine Learning Algorithms

Research Papers on Machine Learning have questioned which machine learning algorithm and what underlying model structure to use has been based on time-consuming investigations and research by human experts.

It has been found out that the right way to select the best algorithms and the most appropriate model architecture, with the correct hyper-parameters, is through trial and error.

Meta-Learning, as it has evolved through the latest research papers on machine learning. It is a concept where exploration of algorithms and model structures take place using machine learning  methods.

For us, learning happens at multiple scales. Our brains are born with the ability to learn new concepts and tasks. Similarly, research papers in Machine Learning show that in Meta-Learning or Learning to Learn, there is a hierarchical application of AI algorithms.

This includes first learning which is the best network architecture, and what optimization algorithms and hyper-parameters are most appropriate for the model that has been selected.

The model that has been selected through this process refines the most mundane of tasks. The research has already achieved remarkable results and with the use of different optimization techniques. Evolutionary Strategies is perhaps the best example of this.

However, with a Meta- Reinforcement Learning Algorithm, the objective is to learn the working behind Reinforcement Learning agent that includes both the Reinforcement Learning algorithm and the policy.

Pieter Abbeel gave an explanation for this at the Meta-Learning Symposium held during NIPS 2017. This was also one of the highest rated research papers on Machine Learning.

Research Papers on Machine Learning: One-Shot Learning

In one of the several research papers in Machine Learning, Oriol Vinyals states that humans are capable of learning new concepts with minimal supervision. In a Deep Learning network, there is a requirement of huge amount of labelled training data because neural networks are still not able to recognize a new object that they have only seen once or twice.

However, more recent researches on machine learning have shown that the application of model-based, or metric-based, or optimization-based Meta-Learning approaches to define network architectures that can learn from just a few data examples.

Moreover, the latest research papers on machine learning, i.e., on One-Shot Learning by Vinyals shows significant improvements have taken place over previous baseline one-shot accuracy for video and language tasks.

This approach uses a model that learns a classifier based on an attention kernel to map a small labelled support set and an unlabelled example to its corresponding label

Again, for Reinforcement Learning applications, One-Shot Imitation Learning brings out the  possibility of learning from just a few demonstrations of a given task. It is possible to generalize to new instances of the same task by applying a Meta-Learning approach to train robust policies.

Research Papers on Machine Learning: Simulation-Based Learning

Several existing Reinforcement Learning (RL) systems, today rely on simulations to explore the solution space and solve complex problems. These include systems based on Self-Play for gaming applications.

Self-Play is an essential part of the algorithms used by Google\DeepMind in AlphaGo. In the more recent AlphaGo Zero reinforcement learning systems. These are some of the breakthrough approaches that have defeated the world champion at the ancient Chinese game of Go.

Thus, it is interesting to note that the newer AlphaGo Zero system has achieved a significant step forward. The training of AlphaGo Zero system was entirely by Self-Play RL starting from a completely random play. It received no human data or supervision input. The system is effectively self-learning.

Therefore, simulation for Reinforcement Learning training has also been used in Imagination Augmented RL algorithms – the recent Imagination-Augmented Agents (I2A) approach improves on the original model-based RL algorithms by combining both model-free and model-based policy rollouts.

Thus, this approach allows the policy improvement & has resulted in a significant improvement in performance.

Research Papers on Machine Learning: The Wasserstein Auto-Encoder

Wasserstein research paper on Auto-Encoders shows how Autoencoders, which are neural networks, are used for dimensionality reduction. Autoencoders are more popularly used  for generative learning models. Variational autoencoder (VAE) is largely used  in applications in image and text recognition space.

Moreover, researchers from Max Planck Institute for Intelligent Systems, Germany, in collaboration with scientists from Google Brain have come up with the Wasserstein Auto encoder (WAE). It is capable of utilizing Wasserstein distance in any generative model.

Their aim was to reduce optimal transport cost function in the model distribution.

Thus, after testing, WAE proved to be more functional. It provided a more stable solution than other auto encoders such as VAE with lesser architectural complexity.

Research Papers on Machine Learning: Ultra-strong Machine Learning Comprehensibility of Programs Learned with ILP

Authors of the paper on Ultra-strong machine learning comprehensibility of programs learned with ILP  are among the most widely read research papers on machine learning algorithms . They  introduced an operational definition for comprehensibility of logic programs. They conducted human trials to determine how properties of a program affect its ease of comprehension.

As a matter of fact, Scholars have used two sets of experiments testing human comprehensibility of logic programs. In the first experiment, they have tested human comprehensibility with and without predicate invention.

Thus, in the second experiment, researchers have directly tested whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in human trials.

The results show that participants were not able to learn the relational concept on their own from a set of examples. They were able to apply the relational definition provided by the ILP system correctly.

Moreover, this implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. The scholars are of opinion that improved understanding of this class could have potential relevance to contexts involving human learning, teaching, and verbal interaction.

Develop your Own Thoughts

While all of the aforementioned papers present a unique perspective in the advancements in machine learning, you must develop your own thoughts on a hot topic and publish it.

The novel methods mentioned in these research papers in machine learning provide diverse avenues for ML research. As a Machine Learning and artificial intelligence enthusiasts, you can gain a lot when it comes to the latest techniques developed in research.

Thus, as a researcher, Machine Learning looks promising as a career option. You may go for a course in MOOC or take up online courses like the John Hopkins Data Science specialization.

Thus, participating in Kaggle or other online machine learning competitions will also help you gain experience. Attending local meetups or academic conferences is always a fruitful way to learn.

Career in Data Science

You may also enroll in a Data Analytics course for more lucrative career options in Data Science . Moreover, Industry-relevant curriculums, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya. Need experts for creating a killer resume that stands out in the crowd?

Thus, for a rewarding career in Machine Learning , one must stay up to date with any up and coming changes. This also means staying abreast of the latest developments for tools, theory and algorithms.

Furthermore, online communities are great places to know of these changes. Also, read a lot. Read articles on Google Map-Reduce, Google File System, Google Big Table, and The  Unreasonable Effectiveness of Data. You will get plenty of free Machine Learning books online. Practice problems, coding competitions, and hackathons are a great way to hone your skills.

Moreover, try finding answers to questions at the end of every research paper on Machine Learning. In addition to research papers in machine learning, subscribe to Machine Learning newsletters or join Machine Learning communities. The latter is better as it helps you gain knowledge through practical implementation of Machine Learning.

Therefore, to build a promising career in Machine Learning, join the Machine Learning Course .

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How to Read Research Papers: A Pragmatic Approach for ML Practitioners

Is it necessary for data scientists or machine-learning experts to read research papers?

The short answer is yes. And don’t worry if you lack a formal academic background or have only obtained an undergraduate degree in the field of machine learning.

Reading academic research papers may be intimidating for individuals without an extensive educational background. However, a lack of academic reading experience should not prevent Data scientists from taking advantage of a valuable source of information and knowledge for machine learning and AI development .

This article provides a hands-on tutorial for data scientists of any skill level to read research papers published in academic journals such as NeurIPS , JMLR , ICML, and so on.

Before diving wholeheartedly into how to read research papers, the first phases of learning how to read research papers cover selecting relevant topics and research papers.

Step 1: Identify a topic

The domain of machine learning and data science is home to a plethora of subject areas that may be studied. But this does not necessarily imply that tackling each topic within machine learning is the best option.

Although generalization for entry-level practitioners is advised, I’m guessing that when it comes to long-term machine learning, career prospects, practitioners, and industry interest often shifts to specialization.

Identifying a niche topic to work on may be difficult, but good. Still, a rule of thumb is to select an ML field in which you are either interested in obtaining a professional position or already have experience.

Deep Learning is one of my interests, and I’m a Computer Vision Engineer that uses deep learning models in apps to solve computer vision problems professionally. As a result, I’m interested in topics like pose estimation, action classification, and gesture identification.

Based on roles, the following are examples of ML/DS occupations and related themes to consider.

For this article, I’ll select the topic Pose Estimation to explore and choose associated research papers to study.

Step 2: Finding research papers

One of the most excellent tools to use while looking at machine learning-related research papers, datasets, code, and other related materials is PapersWithCode .

We use the search engine on the PapersWithCode website to get relevant research papers and content for our chosen topic, “Pose Estimation.” The following image shows you how it’s done.

The search results page contains a short explanation of the searched topic, followed by a table of associated datasets, models, papers, and code. Without going into too much detail, the area of interest for this use case is the “Greatest papers with code”. This section contains the relevant papers related to the task or topic. For the purpose of this article, I’ll select the DensePose: Dense Human Pose Estimation In The Wild .

Step 3: First pass (gaining context and understanding)

At this point, we’ve selected a research paper to study and are prepared to extract any valuable learnings and findings from its content.

It’s only natural that your first impulse is to start writing notes and reading the document from beginning to end, perhaps taking some rest in between. However, having a context for the content of a study paper is a more practical way to read it. The title, abstract, and conclusion are three key parts of any research paper to gain an understanding.

The goal of the first pass of your chosen paper is to achieve the following:

• Assure that the paper is relevant.
• Obtain a sense of the paper’s context by learning about its contents, methods, and findings.
• Recognize the author’s goals, methodology, and accomplishments.

The title is the first point of information sharing between the authors and the reader. Therefore, research papers titles are direct and composed in a manner that leaves no ambiguity.

The research paper title is the most telling aspect since it indicates the study’s relevance to your work. The importance of the title is to give a brief perception of the paper’s content.

In this situation, the title is “DensePose: Dense Human Pose Estimation in the Wild.” This gives a broad overview of the work and implies that it will look at how to provide pose estimations in environments with high levels of activity and realistic situations properly.

The abstract portion gives a summarized version of the paper. It’s a short section that contains 300-500 words and tells you what the paper is about in a nutshell. The abstract is a brief text that provides an overview of the article’s content, researchers’ objectives, methods, and techniques.

When reading an abstract of a machine-learning research paper, you’ll typically come across mentions of datasets, methods, algorithms, and other terms. Keywords relevant to the article’s content provide context. It may be helpful to take notes and keep track of all keywords at this point.

For the paper: “ DensePose: Dense Human Pose Estimation In The Wild “, I identified in the abstract the following keywords: pose estimation, COCO dataset, CNN, region-based models, real-time.

It’s not uncommon to experience fatigue when reading the paper from top to bottom at your first initial pass, especially for Data Scientists and practitioners with no prior advanced academic experience. Although extracting information from the later sections of a paper might seem tedious after a long study session, the conclusion sections are often short. Hence reading the conclusion section in the first pass is recommended.

The conclusion section is a brief compendium of the work’s author or authors and/or contributions and accomplishments and promises for future developments and limitations.

Before reading the main content of a research paper, read the conclusion section to see if the researcher’s contributions, problem domain, and outcomes match your needs.

Following this particular brief first pass step enables a sufficient understanding and overview of the research paper’s scope and objectives, as well as a context for its content. You’ll be able to get more detailed information out of its content by going through it again with laser attention.

Step 4: Second pass (content familiarization)

Content familiarization is a process that’s relevant to the initial steps. The systematic approach to reading the research paper presented in this article. The familiarity process is a step that involves the introduction section and figures within the research paper.

As previously mentioned, the urge to plunge straight into the core of the research paper is not required because knowledge acclimatization provides an easier and more comprehensive examination of the study in later passes.

Introduction

Introductory sections of research papers are written to provide an overview of the objective of the research efforts. This objective mentions and explains problem domains, research scope, prior research efforts, and methodologies.

It’s normal to find parallels to past research work in this area, using similar or distinct methods. Other papers’ citations provide the scope and breadth of the problem domain, which broadens the exploratory zone for the reader. Perhaps incorporating the procedure outlined in Step 3 is sufficient at this point.

Another aspect of the benefit provided by the introduction section is the presentation of requisite knowledge required to approach and understand the content of the research paper.

Graph, diagrams, figures

Illustrative materials within the research paper ensure that readers can comprehend factors that support problem definition or explanations of methods presented. Commonly, tables are used within research papers to provide information on the quantitative performances of novel techniques in comparison to similar approaches.

Generally, the visual representation of data and performance enables the development of an intuitive understanding of the paper’s context. In the Dense Pose paper mentioned earlier, illustrations are used to depict the performance of the author’s approach to pose estimation and create. An overall understanding of the steps involved in generating and annotating data samples.

In the realm of deep learning, it’s common to find topological illustrations depicting the structure of artificial neural networks. Again this adds to the creation of intuitive understanding for any reader. Through illustrations and figures, readers may interpret the information themselves and gain a fuller perspective of it without having any preconceived notions about what outcomes should be.

Step 5: Third pass (deep reading)

The third pass of the paper is similar to the second, though it covers a greater portion of the text. The most important thing about this pass is that you avoid any complex arithmetic or technique formulations that may be difficult for you. During this pass, you can also skip over any words and definitions that you don’t understand or aren’t familiar with. These unfamiliar terms, algorithms, or techniques should be noted to return to later.

During this pass, your primary objective is to gain a broad understanding of what’s covered in the paper. Approach the paper, starting again from the abstract to the conclusion, but be sure to take intermediary breaks in between sections. Moreover, it’s recommended to have a notepad, where all key insights and takeaways are noted, alongside the unfamiliar terms and concepts.

The Pomodoro Technique is an effective method of managing time allocated to deep reading or study. Explained simply, the Pomodoro Technique involves the segmentation of the day into blocks of work, followed by short breaks.

What works for me is the 50/15 split, that is, 50 minutes studying and 15 minutes allocated to breaks. I tend to execute this split twice consecutively before taking a more extended break of 30 minutes. If you are unfamiliar with this time management technique, adopt a relatively easy division such as 25/5 and adjust the time split according to your focus and time capacity.

Step 6: Forth pass (final pass)

The final pass is typically one that involves an exertion of your mental and learning abilities, as it involves going through the unfamiliar terms, terminologies, concepts, and algorithms noted in the previous pass. This pass focuses on using external material to understand the recorded unfamiliar aspects of the paper.

In-depth studies of unfamiliar subjects have no specified time length, and at times efforts span into the days and weeks. The critical factor to a successful final pass is locating the appropriate sources for further exploration.

 Unfortunately, there isn’t one source on the Internet that provides the wealth of information you require. Still, there are multiple sources that, when used in unison and appropriately, fill knowledge gaps. Below are a few of these resources.

• The Machine Learning Subreddit
• The Deep Learning Subreddit
• PapersWithCode
• Top conferences such as NIPS , ICML , ICLR
• Research Gate
• Machine Learning Apple

The Reference sections of research papers mention techniques and algorithms. Consequently, the current paper either draws inspiration from or builds upon, which is why the reference section is a useful source to use in your deep reading sessions.

Step 7: Summary (optional)

In almost a decade of academic and professional undertakings of technology-associated subjects and roles, the most effective method of ensuring any new information learned is retained in my long-term memory through the recapitulation of explored topics. By rewriting new information in my own words, either written or typed, I’m able to reinforce the presented ideas in an understandable and memorable manner.

To take it one step further, it’s possible to publicize learning efforts and notes through the utilization of blogging platforms and social media. An attempt to explain the freshly explored concept to a broad audience, assuming a reader isn’t accustomed to the topic or subject, requires understanding topics in intrinsic details.

Undoubtedly, reading research papers for novice Data Scientists and ML practitioners can be daunting and challenging; even seasoned practitioners find it difficult to digest the content of research papers in a single pass successfully.

The nature of the Data Science profession is very practical and involved. Meaning, there’s a requirement for its practitioners to employ an academic mindset, more so as the Data Science domain is closely associated with AI, which is still a developing field.

To summarize, here are all of the steps you should follow to read a research paper:

• Identify A Topic.
• Finding associated Research Papers
• Read title, abstract, and conclusion to gain a vague understanding of the research effort aims and achievements.
• Familiarize yourself with the content by diving deeper into the introduction; including the exploration of figures and graphs presented in the paper.
• Use a deep reading session to digest the main content of the paper as you go through the paper from top to bottom.
• Explore unfamiliar terms, terminologies, concepts, and methods using external resources.
• Summarize in your own words essential takeaways, definitions, and algorithms.

Thanks for reading!

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