Reinforcement learning is a very useful (and currently popular) subtype of machine learning and artificial intelligence. It is based on the principle that agents, when placed in an interactive environment, can learn from their actions via rewards associated with the actions, and improve the time to achieve their goal.
In this article, we’ll explore the fundamental concepts of reinforcement learning and discuss its key components, types, and applications.
Definition of Reinforcement Learning
We can define reinforcement learning as a machine learning technique involving an agent who needs to decide which actions it needs to do to perform a task that has been assigned to it most effectively. For this, rewards are assigned to the different actions that the agent can take at different situations or states of the environment. Initially, the agent has no idea about the best or correct actions. Using reinforcement learning, it explores its action choices via trial and error and figures out the best set of actions for completing its assigned task.
The basic idea behind a reinforcement learning agent is to learn from experience. Just like humans learn lessons from their past successes and mistakes, reinforcement learning agents do the same – when they do something “good” they get a reward, but, if they do something “bad”, they get penalized. The reward reinforces the good actions while the penalty avoids the bad ones.
Reinforcement learning requires several key components:
- Agent – This is the “who” or the subject of the process, which performs different actions to perform a task that has been assigned to it.
- Environment – This is the “where” or a situation in which the agent is placed.
- Actions – This is the “what” or the steps an agent needs to take to reach the goal.
- Rewards – This is the feedback an agent receives after performing an action.
Before we dig deep into the technicalities, let’s warm up with a real-life example. Reinforcement isn’t new, and we’ve used it for different purposes for centuries. One of the most basic examples is dog training.
Let’s say you’re in a park, trying to teach your dog to fetch a ball. In this case, the dog is the agent, and the park is the environment. Once you throw the ball, the dog will run to catch it, and that’s the action part. When he brings the ball back to you and releases it, he’ll get a reward (a treat). Since he got a reward, the dog will understand that his actions were appropriate and will repeat them in the future. If the dog doesn’t bring the ball back, he may get some “punishment” – you may ignore him or say “No!” After a few attempts (or more than a few, depending on how stubborn your dog is), the dog will fetch the ball with ease.
We can say that the reinforcement learning process has three steps:
- Interaction
- Learning
- Decision-making
Types of Reinforcement Learning
There are two types of reinforcement learning: model-based and model-free.
Model-Based Reinforcement Learning
With model-based reinforcement learning (RL), there’s a model that an agent uses to create additional experiences. Think of this model as a mental image that the agent can analyze to assess whether particular strategies could work.
Some of the advantages of this RL type are:
- It doesn’t need a lot of samples.
- It can save time.
- It offers a safe environment for testing and exploration.
The potential drawbacks are:
- Its performance relies on the model. If the model isn’t good, the performance won’t be good either.
- It’s quite complex.
Model-Free Reinforcement Learning
In this case, an agent doesn’t rely on a model. Instead, the basis for its actions lies in direct interactions with the environment. An agent tries different scenarios and tests whether they’re successful. If yes, the agent will keep repeating them. If not, it will try another scenario until it finds the right one.
What are the advantages of model-free reinforcement learning?
- It doesn’t depend on a model’s accuracy.
- It’s not as computationally complex as model-based RL.
- It’s often better for real-life situations.
Some of the drawbacks are:
- It requires more exploration, so it can be more time-consuming.
- It can be dangerous because it relies on real-life interactions.
Model-Based vs. Model-Free Reinforcement Learning: Example
Understanding model-based and model-free RL can be challenging because they often seem too complex and abstract. We’ll try to make the concepts easier to understand through a real-life example.
Let’s say you have two soccer teams that have never played each other before. Therefore, neither of the teams knows what to expect. At the beginning of the match, Team A tries different strategies to see whether they can score a goal. When they find a strategy that works, they’ll keep using it to score more goals. This is model-free reinforcement learning.
On the other hand, Team B came prepared. They spent hours investigating strategies and examining the opponent. The players came up with tactics based on their interpretation of how Team A will play. This is model-based reinforcement learning.
Who will be more successful? There’s no way to tell. Team B may be more successful in the beginning because they have previous knowledge. But Team A can catch up quickly, especially if they use the right tactics from the start.
Reinforcement Learning Algorithms
A reinforcement learning algorithm specifies how an agent learns suitable actions from the rewards. RL algorithms are divided into two categories: value-based and policy gradient-based.
Value-Based Algorithms
Value-based algorithms learn the value at each state of the environment, where the value of a state is given by the expected rewards to complete the task while starting from that state.
Q-Learning
This model-free, off-policy RL algorithm focuses on providing guidelines to the agent on what actions to take and under what circumstances to win the reward. The algorithm uses Q-tables in which it calculates the potential rewards for different state-action pairs in the environment. The table contains Q-values that get updated after each action during the agent’s training. During execution, the agent goes back to this table to see which actions have the best value.
Deep Q-Networks (DQN)
Deep Q-networks, or deep q-learning, operate similarly to q-learning. The main difference is that the algorithm in this case is based on neural networks.
SARSA
The acronym stands for state-action-reward-state-action. SARSA is an on-policy RL algorithm that uses the current action from the current policy to learn the value.
Policy-Based Algorithms
These algorithms directly update the policy to maximize the reward. There are different policy gradient-based algorithms: REINFORCE, proximal policy optimization, trust region policy optimization, actor-critic algorithms, advantage actor-critic, deep deterministic policy gradient (DDPG), and twin-delayed DDPG.
Examples of Reinforcement Learning Applications
The advantages of reinforcement learning have been recognized in many spheres. Here are several concrete applications of RL.
Robotics and Automation
With RL, robotic arms can be trained to perform human-like tasks. Robotic arms can give you a hand in warehouse management, packaging, quality testing, defect inspection, and many other aspects.
Another notable role of RL lies in automation, and self-driving cars are an excellent example. They’re introduced to different situations through which they learn how to behave in specific circumstances and offer better performance.
Gaming and Entertainment
Gaming and entertainment industries certainly benefit from RL in many ways. From AlphaGo (the first program that has beaten a human in the board game Go) to video games AI, RL offers limitless possibilities.
Finance and Trading
RL can optimize and improve trading strategies, help with portfolio management, minimize risks that come with running a business, and maximize profit.
Healthcare and Medicine
RL can help healthcare workers customize the best treatment plan for their patients, focusing on personalization. It can also play a major role in drug discovery and testing, allowing the entire sector to get one step closer to curing patients quickly and efficiently.
Basics for Implementing Reinforcement Learning
The success of reinforcement learning in a specific area depends on many factors.
First, you need to analyze a specific situation and see which RL algorithm suits it. Your job doesn’t end there; now you need to define the environment and the agent and figure out the right reward system. Without them, RL doesn’t exist. Next, allow the agent to put its detective cap on and explore new features, but ensure it uses the existing knowledge adequately (strike the right balance between exploration and exploitation). Since RL changes rapidly, you want to keep your model updated. Examine it every now and then to see what you can tweak to keep your model in top shape.
Explore the World of Possibilities With Reinforcement Learning
Reinforcement learning goes hand-in-hand with the development and modernization of many industries. We’ve been witnesses to the incredible things RL can achieve when used correctly, and the future looks even better. Hop in on the RL train and immerse yourself in this fascinating world.
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From the same author
Read the articleReinforcement learning is a very useful (and currently popular) subtype of machine learning and artificial intelligence. It is based on the principle that agents, when placed in an interactive environment, can learn from their actions via rewards associated with the actions, and improve the time to achieve their goal.
In this article, we’ll explore the fundamental concepts of reinforcement learning and discuss its key components, types, and applications.
Definition of Reinforcement Learning
We can define reinforcement learning as a machine learning technique involving an agent who needs to decide which actions it needs to do to perform a task that has been assigned to it most effectively. For this, rewards are assigned to the different actions that the agent can take at different situations or states of the environment. Initially, the agent has no idea about the best or correct actions. Using reinforcement learning, it explores its action choices via trial and error and figures out the best set of actions for completing its assigned task.
The basic idea behind a reinforcement learning agent is to learn from experience. Just like humans learn lessons from their past successes and mistakes, reinforcement learning agents do the same – when they do something “good” they get a reward, but, if they do something “bad”, they get penalized. The reward reinforces the good actions while the penalty avoids the bad ones.
Reinforcement learning requires several key components:
- Agent – This is the “who” or the subject of the process, which performs different actions to perform a task that has been assigned to it.
- Environment – This is the “where” or a situation in which the agent is placed.
- Actions – This is the “what” or the steps an agent needs to take to reach the goal.
- Rewards – This is the feedback an agent receives after performing an action.
Before we dig deep into the technicalities, let’s warm up with a real-life example. Reinforcement isn’t new, and we’ve used it for different purposes for centuries. One of the most basic examples is dog training.
Let’s say you’re in a park, trying to teach your dog to fetch a ball. In this case, the dog is the agent, and the park is the environment. Once you throw the ball, the dog will run to catch it, and that’s the action part. When he brings the ball back to you and releases it, he’ll get a reward (a treat). Since he got a reward, the dog will understand that his actions were appropriate and will repeat them in the future. If the dog doesn’t bring the ball back, he may get some “punishment” – you may ignore him or say “No!” After a few attempts (or more than a few, depending on how stubborn your dog is), the dog will fetch the ball with ease.
We can say that the reinforcement learning process has three steps:
- Interaction
- Learning
- Decision-making
Types of Reinforcement Learning
There are two types of reinforcement learning: model-based and model-free.
Model-Based Reinforcement Learning
With model-based reinforcement learning (RL), there’s a model that an agent uses to create additional experiences. Think of this model as a mental image that the agent can analyze to assess whether particular strategies could work.
Some of the advantages of this RL type are:
- It doesn’t need a lot of samples.
- It can save time.
- It offers a safe environment for testing and exploration.
The potential drawbacks are:
- Its performance relies on the model. If the model isn’t good, the performance won’t be good either.
- It’s quite complex.
Model-Free Reinforcement Learning
In this case, an agent doesn’t rely on a model. Instead, the basis for its actions lies in direct interactions with the environment. An agent tries different scenarios and tests whether they’re successful. If yes, the agent will keep repeating them. If not, it will try another scenario until it finds the right one.
What are the advantages of model-free reinforcement learning?
- It doesn’t depend on a model’s accuracy.
- It’s not as computationally complex as model-based RL.
- It’s often better for real-life situations.
Some of the drawbacks are:
- It requires more exploration, so it can be more time-consuming.
- It can be dangerous because it relies on real-life interactions.
Model-Based vs. Model-Free Reinforcement Learning: Example
Understanding model-based and model-free RL can be challenging because they often seem too complex and abstract. We’ll try to make the concepts easier to understand through a real-life example.
Let’s say you have two soccer teams that have never played each other before. Therefore, neither of the teams knows what to expect. At the beginning of the match, Team A tries different strategies to see whether they can score a goal. When they find a strategy that works, they’ll keep using it to score more goals. This is model-free reinforcement learning.
On the other hand, Team B came prepared. They spent hours investigating strategies and examining the opponent. The players came up with tactics based on their interpretation of how Team A will play. This is model-based reinforcement learning.
Who will be more successful? There’s no way to tell. Team B may be more successful in the beginning because they have previous knowledge. But Team A can catch up quickly, especially if they use the right tactics from the start.
Reinforcement Learning Algorithms
A reinforcement learning algorithm specifies how an agent learns suitable actions from the rewards. RL algorithms are divided into two categories: value-based and policy gradient-based.
Value-Based Algorithms
Value-based algorithms learn the value at each state of the environment, where the value of a state is given by the expected rewards to complete the task while starting from that state.
Q-Learning
This model-free, off-policy RL algorithm focuses on providing guidelines to the agent on what actions to take and under what circumstances to win the reward. The algorithm uses Q-tables in which it calculates the potential rewards for different state-action pairs in the environment. The table contains Q-values that get updated after each action during the agent’s training. During execution, the agent goes back to this table to see which actions have the best value.
Deep Q-Networks (DQN)
Deep Q-networks, or deep q-learning, operate similarly to q-learning. The main difference is that the algorithm in this case is based on neural networks.
SARSA
The acronym stands for state-action-reward-state-action. SARSA is an on-policy RL algorithm that uses the current action from the current policy to learn the value.
Policy-Based Algorithms
These algorithms directly update the policy to maximize the reward. There are different policy gradient-based algorithms: REINFORCE, proximal policy optimization, trust region policy optimization, actor-critic algorithms, advantage actor-critic, deep deterministic policy gradient (DDPG), and twin-delayed DDPG.
Examples of Reinforcement Learning Applications
The advantages of reinforcement learning have been recognized in many spheres. Here are several concrete applications of RL.
Robotics and Automation
With RL, robotic arms can be trained to perform human-like tasks. Robotic arms can give you a hand in warehouse management, packaging, quality testing, defect inspection, and many other aspects.
Another notable role of RL lies in automation, and self-driving cars are an excellent example. They’re introduced to different situations through which they learn how to behave in specific circumstances and offer better performance.
Gaming and Entertainment
Gaming and entertainment industries certainly benefit from RL in many ways. From AlphaGo (the first program that has beaten a human in the board game Go) to video games AI, RL offers limitless possibilities.
Finance and Trading
RL can optimize and improve trading strategies, help with portfolio management, minimize risks that come with running a business, and maximize profit.
Healthcare and Medicine
RL can help healthcare workers customize the best treatment plan for their patients, focusing on personalization. It can also play a major role in drug discovery and testing, allowing the entire sector to get one step closer to curing patients quickly and efficiently.
Basics for Implementing Reinforcement Learning
The success of reinforcement learning in a specific area depends on many factors.
First, you need to analyze a specific situation and see which RL algorithm suits it. Your job doesn’t end there; now you need to define the environment and the agent and figure out the right reward system. Without them, RL doesn’t exist. Next, allow the agent to put its detective cap on and explore new features, but ensure it uses the existing knowledge adequately (strike the right balance between exploration and exploitation). Since RL changes rapidly, you want to keep your model updated. Examine it every now and then to see what you can tweak to keep your model in top shape.
Explore the World of Possibilities With Reinforcement Learning
Reinforcement learning goes hand-in-hand with the development and modernization of many industries. We’ve been witnesses to the incredible things RL can achieve when used correctly, and the future looks even better. Hop in on the RL train and immerse yourself in this fascinating world.
Read the articleAlgorithms are the backbone behind technology that have helped establish some of the world’s most famous companies. Software giants like Google, beverage giants Coca Cola and many other organizations utilize proprietary algorithms to improve their services and enhance customer experience. Algorithms are an inseparable part of the technology behind organization as they help improve security, product or service recommendations, and increase sales.
Knowing the benefits of algorithms is useful, but you might also be interested to know what makes them so advantageous. As such, you’re probably asking: “What is an algorithm?” Here’s the most common algorithm definition: an algorithm is a set of procedures and rules a computer follows to solve a problem.
In addition to the meaning of the word “algorithm,” this article will also cover the key types and characteristics of algorithms, as well as their applications.
Types of Algorithms and Design Techniques
One of the main reasons people rely on algorithms is that they offer a principled and structured means to represent a problem on a computer.
Recursive Algorithms
Recursive algorithms are critical for solving many problems. The core idea behind recursive algorithms is to use functions that call themselves on smaller chunks of the problem.
Divide and Conquer Algorithms
Divide and conquer algorithms are similar to recursive algorithms. They divide a large problem into smaller units. Algorithms solve each smaller component before combining them to tackle the original, large problem.
Greedy Algorithms
A greedy algorithm looks for solutions based on benefits. More specifically, it resolves problems in sections by determining how many benefits it can extract by analyzing a certain section. The more benefits it has, the more likely it is to solve a problem, hence the term greedy.
Dynamic Programming Algorithms
Dynamic programming algorithms follow a similar approach to recursive and divide and conquer algorithms. First, they break down a complex problem into smaller pieces. Next, it solves each smaller piece once and saves the solution for later use instead of computing it.
Backtracking Algorithms
After dividing a problem, an algorithm may have trouble moving forward to find a solution. If that’s the case, a backtracking algorithm can return to parts of the problem it has already solved until it determines a way forward that can overcome the setback.
Brute Force Algorithms
Brute force algorithms try every possible solution until they determine the best one. Brute force algorithms are simpler, but the solution they find might not be as good or elegant as those found by the other types of algorithms.
Algorithm Analysis and Optimization
Digital transformation remains one of the biggest challenges for businesses in 2023. Algorithms can facilitate the transition through careful analysis and optimization.
Time Complexity
The time complexity of an algorithm refers to how long you need to execute a certain algorithm. A number of factors determine time complexity, but the algorithm’s input length is the most important consideration.
Space Complexity
Before you can run an algorithm, you need to make sure your device has enough memory. The amount of memory required for executing an algorithm is known as space complexity.
Trade-Offs
Solving a problem with an algorithm in C or any other programming language is about making compromises. In other words, the system often makes trade-offs between the time and space available.
For example, an algorithm can use less space, but this extends the time it takes to solve a problem. Alternatively, it can take up a lot of space to address an issue faster.
Optimization Techniques
Algorithms generally work great out of the box, but they sometimes fail to deliver the desired results. In these cases, you can implement a slew of optimization techniques to make them more effective.
Memorization
You generally use memorization if you wish to elevate the efficacy of a recursive algorithm. The technique rewrites algorithms and stores them in arrays. The main reason memorization is so powerful is that it eliminates the need to calculate results multiple times.
Parallelization
As the name suggests, parallelization is the ability of algorithms to perform operations simultaneously. This accelerates task completion and is normally utilized when you have a lot of memory on your device.
Heuristics
Heuristic algorithms (a.k.a. heuristics) are algorithms used to speed up problem-solving. They generally target non-deterministic polynomial-time (NP) problems.
Approximation Algorithms
Another way to solve a problem if you’re short on time is to incorporate an approximation algorithm. Rather than provide a 100% optimal solution and risk taking longer, you use this algorithm to get approximate solutions. From there, you can calculate how far away they are from the optimal solution.
Pruning
Algorithms sometimes analyze unnecessary data, slowing down your task completion. A great way to expedite the process is to utilize pruning. This compression method removes unwanted information by shrinking algorithm decision trees.
Algorithm Applications and Challenges
Thanks to this introduction to algorithm, you’ll no longer wonder: “What is an algorithm, and what are the different types?” Now it’s time to go through the most significant applications and challenges of algorithms.
Sorting Algorithms
Sorting algorithms arrange elements in a series to help solve complex issues faster. There are different types of sorting, including linear, insertion, and bubble sorting. They’re generally used for exploring databases and virtual search spaces.
Searching Algorithms
An algorithm in C or other programming languages can be used as a searching algorithm. They allow you to identify a small item in a large group of related elements.
Graph Algorithms
Graph algorithms are just as practical, if not more practical, than other types. Graphs consist of nodes and edges, where each edge connects two nodes.
There are numerous real-life applications of graph algorithms. For instance, you might have wondered how engineers solve problems regarding wireless networks or city traffic. The answer lies in using graph algorithms.
The same goes for social media sites, such as Facebook. Algorithms on such platforms contain nodes, which represent key information, like names and genders and edges that represent the relationships or dependencies between them.
Cryptography Algorithms
When creating an account on some websites, the platform can generate a random password for you. It’s usually stronger than custom-made codes, thanks to cryptography algorithms. They can scramble digital text and turn it into an unreadable string. Many organizations use this method to protect their data and prevent unauthorized access.
Machine Learning Algorithms
Over 70% of enterprises prioritize machine learning applications. To implement their ideas, they rely on machine learning algorithms. They’re particularly useful for financial institutions because they can predict future trends.
Famous Algorithm Challenges
Many organizations struggle to adopt algorithms, be it an algorithm in data structure or computer science. The reason being, algorithms present several challenges:
- Opacity – You can’t take a closer look at the inside of an algorithm. Only the end result is visible, which is why it’s difficult to understand an algorithm.
- Heterogeneity – Most algorithms are heterogeneous, behaving differently from one another. This makes them even more complex.
- Dependency – Each algorithm comes with the abovementioned time and space restrictions.
Algorithm Ethics, Fairness, and Social Impact
When discussing critical characteristics of algorithms, it’s important to highlight the main concerns surrounding this technology.
Bias in Algorithms
Algorithms aren’t intrinsically biased unless the developer injects their personal biases into the design. If so, getting impartial results from an algorithm is highly unlikely.
Transparency and Explainability
Knowing only the consequences of algorithms prevents us from explaining them in detail. A transparent algorithm enables a user to view and understand its different operations. In contrast, explainability of an algorithm relates to its ability to provide reasons for the decisions it makes.
Privacy and Security
Some algorithms require end users to share private information. If cyber criminals hack the system, they can easily steal the data.
Algorithm Accessibility and Inclusivity
Limited explainability hinders access to algorithms. Likewise, it’s hard to include different viewpoints and characteristics in an algorithm, especially if it is biased.
Algorithm Trust and Confidence
No algorithm is omnipotent. Claiming otherwise makes it untrustworthy – the best way to prevent this is for the algorithm to state its limitations.
Algorithm Social Impact
Algorithms impact almost every area of life including politics, economic and healthcare decisions, marketing, transportation, social media and Internet, and society and culture in general.
Algorithm Sustainability and Environmental Impact
Contrary to popular belief, algorithms aren’t very sustainable. The extraction of materials to make computers that power algorithms is a major polluter.
Future of Algorithms
Algorithms are already advanced, but what does the future hold for this technology? Here are a few potential applications and types of future algorithms:
- Quantum Algorithms – Quantum algorithms are expected to run on quantum computers to achieve unprecedented speeds and efficiency.
- Artificial Intelligence and Machine Learning – AI and machine learning algorithms can help a computer develop human-like cognitive qualities via learning from its environment and experiences.
- Algorithmic Fairness and Ethics – Considering the aforementioned challenges of algorithms, developers are expected to improve the technology. It may become more ethical with fewer privacy violations and accessibility issues.
Smart, Ethical Implementation Is the Difference-Maker
Understanding algorithms is crucial if you want to implement them correctly and ethically. They’re powerful, but can also have unpleasant consequences if you’re not careful during the development stage. Responsible use is paramount because it can improve many areas, including healthcare, economics, social media, and communication.
If you wish to learn more about algorithms, accredited courses might be your best option. AI and machine learning-based modules cover some of the most widely-used algorithms to help expand your knowledge about this topic.
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From personalization to productivity: AI at the heart of the educational experience.
Click this link to read and download the e-book.
At its core, teaching is a simple endeavour. The experienced and learned pass on their knowledge and wisdom to new generations. Nothing has changed in that regard. What has changed is how new technologies emerge to facilitate that passing on of knowledge. The printing press, computers, the internet – all have transformed how educators teach and how students learn.
Artificial intelligence (AI) is the next game-changer in the educational space.
Specifically, AI agents have emerged as tools that utilize all of AI’s core strengths, such as data gathering and analysis, pattern identification, and information condensing. Those strengths have been refined, first into simple chatbots capable of providing answers, and now into agents capable of adapting how they learn and adjusting to the environment in which they’re placed. This adaptability, in particular, makes AI agents vital in the educational realm.
The reasons why are simple. AI agents can collect, analyse, and condense massive amounts of educational material across multiple subject areas. More importantly, they can deliver that information to students while observing how the students engage with the material presented. Those observations open the door for tweaks. An AI agent learns alongside their student. Only, the agent’s learning focuses on how it can adapt its delivery to account for a student’s strengths, weaknesses, interests, and existing knowledge.
Think of an AI agent like having a tutor – one who eschews set lesson plans in favour of an adaptive approach designed and tweaked constantly for each specific student.
In this eBook, the Open Institute of Technology (OPIT) will take you on a journey through the world of AI agents as they pertain to education. You will learn what these agents are, how they work, and what they’re capable of achieving in the educational sector. We also explore best practices and key approaches, focusing on how educators can use AI agents to the benefit of their students. Finally, we will discuss other AI tools that both complement and enhance an AI agent’s capabilities, ensuring you deliver the best possible educational experience to your students.
The Open Institute of Technology (OPIT) began enrolling students in 2023 to help bridge the skills gap between traditional university education and the requirements of the modern workplace. OPIT’s MSc courses aim to help professionals make a greater impact on their workplace through technology.
OPIT’s courses have become popular with business leaders hoping to develop a strong technical foundation to understand technologies, such as artificial intelligence (AI) and cybersecurity, that are shaping their industry. But OPIT is also attracting professionals with strong technical expertise looking to engage more deeply with the strategic side of digital innovation. This is the story of one such student, Obiora Awogu.
Meet Obiora
Obiora Awogu is a cybersecurity expert from Nigeria with a wealth of credentials and experience from working in the industry for a decade. Working in a lead data security role, he was considering “what’s next” for his career. He was contemplating earning an MSc to add to his list of qualifications he did not yet have, but which could open important doors. He discussed the idea with his mentor, who recommended OPIT, where he himself was already enrolled in an MSc program.
Obiora started looking at the program as a box-checking exercise, but quickly realized that it had so much more to offer. As well as being a fully EU-accredited course that could provide new opportunities with companies around the world, he recognized that the course was designed for people like him, who were ready to go from building to leading.
OPIT’s MSc in Cybersecurity
OPIT’s MSc in Cybersecurity launched in 2024 as a fully online and flexible program ideal for busy professionals like Obiora who want to study without taking a career break.
The course integrates technical and leadership expertise, equipping students to not only implement cybersecurity solutions but also lead cybersecurity initiatives. The curriculum combines technical training with real-world applications, emphasizing hands-on experience and soft skills development alongside hard technical know-how.
The course is led by Tom Vazdar, the Area Chair for Cybersecurity at OPIT, as well as the Chief Security Officer at Erste Bank Croatia and an Advisory Board Member for EC3 European Cybercrime Center. He is representative of the type of faculty OPIT recruits, who are both great teachers and active industry professionals dealing with current challenges daily.
Experts such as Matthew Jelavic, the CEO at CIM Chartered Manager Canada and President of Strategy One Consulting; Mahynour Ahmed, Senior Cloud Security Engineer at Grant Thornton LLP; and Sylvester Kaczmarek, former Chief Scientific Officer at We Space Technologies, join him.
Course content includes:
- Cybersecurity fundamentals and governance
- Network security and intrusion detection
- Legal aspects and compliance
- Cryptography and secure communications
- Data analytics and risk management
- Generative AI cybersecurity
- Business resilience and response strategies
- Behavioral cybersecurity
- Cloud and IoT security
- Secure software development
- Critical thinking and problem-solving
- Leadership and communication in cybersecurity
- AI-driven forensic analysis in cybersecurity
As with all OPIT’s MSc courses, it wraps up with a capstone project and dissertation, which sees students apply their skills in the real world, either with their existing company or through apprenticeship programs. This not only gives students hands-on experience, but also helps them demonstrate their added value when seeking new opportunities.
Obiora’s Experience
Speaking of his experience with OPIT, Obiora said that it went above and beyond what he expected. He was not surprised by the technical content, in which he was already well-versed, but rather the change in perspective that the course gave him. It helped him move from seeing himself as someone who implements cybersecurity solutions to someone who could shape strategy at the highest levels of an organization.
OPIT’s MSc has given Obiora the skills to speak to boards, connect risk with business priorities, and build organizations that don’t just defend against cyber risks but adapt to a changing digital world. He commented that studying at OPIT did not give him answers; instead, it gave him better questions and the tools to lead. Of course, it also ticks the MSc box, and while that might not be the main reason for studying at OPIT, it is certainly a clear benefit.
Obiora has now moved into a leading Chief Information Security Officer Role at MoMo, Payment Service Bank for MTN. There, he is building cyber-resilient financial systems, contributing to public-private partnerships, and mentoring the next generation of cybersecurity experts.
Leading Cybersecurity in Africa
As well as having a significant impact within his own organization, studying at OPIT has helped Obiora develop the skills and confidence needed to become a leader in the cybersecurity industry across Africa.
In March 2025, Obiora was featured on the cover of CIO Africa Magazine and was then a panelist on the “Future of Cybersecurity Careers in the Age of Generative AI” for Comercio Ltd. The Lagos Chamber of Commerce and Industry also invited him to speak on Cybersecurity in Africa.
Obiora recently presented the keynote speech at the Hackers Secret Conference 2025 on “Code in the Shadows: Harnessing the Human-AI Partnership in Cybersecurity.” In the talk, he explored how AI is revolutionizing incident response, enhancing its speed, precision, and proactivity, and improving on human-AI collaboration.
An OPIT Success Story
Talking about Obiora’s success, the OPIT Area Chair for Cybersecurity said:
“Obiora is a perfect example of what this program was designed for – experienced professionals ready to scale their impact beyond operations. It’s been inspiring to watch him transform technical excellence into strategic leadership. Africa’s cybersecurity landscape is stronger with people like him at the helm. Bravo, Obiora!”
Learn more about OPIT’s MSc in Cybersecurity and how it can support the next steps of your career.
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