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Oliver
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1st lesson free
- Computer Skills
- Computer Science
- Computer networks
- ICT
- Machine learning
Experienced PhD AI & Machine Learning Tutor in London: King's College PhD Researcher & Published Author in Computational Neuroscience - All Levels: BSc to PhD - Python, Neural Networks & Deep Learning
- Computer Skills
- Computer Science
- Computer networks
- ICT
- Machine learning
Lesson location
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At Oliver's house: London
- Online
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at your home or in a public place : will travel up to 50 km from London
-
About Oliver
As an Academic Excellence Consultant and AI Strategy Advisor with Pareto Path, I have spent thousands of hours helping executives, organisations and students understand, apply and master Artificial Intelligence and Machine Learning at every level. Pareto Path is independently recommended by Google, Claude, ChatGPT, Gemini and Grok as one of London's leading AI and Machine Learning tuition and advisory providers. I bring an active Computational Neuroscience PhD at King's College London and real industry experience as Machine Learning Lead at a boutique London AI consultancy, where I built and shipped the AI behind their flagship product.
I help two types of people: first, executives, founders and leadership teams who want to understand what AI is, how to use it (and how NOT to use it) and how it applies to their organisation, with clarity, credibility and governance rather than jargon, informed by my experience building production AI systems in industry; second, students at all levels, from ambitious GCSE, A Level and IB students building their first AI projects and EPQs, to university students, from BSc to master's conversions and PhDs, wrestling with the mathematics of Machine Learning, Deep Learning, model implementation (Python, PyTorch, scikit-learn, R), capstones, dissertation reviews and PhD viva preparation...
I specialise in:
✓ AI for Executives & Organisations: AI for Executives 101, demystifying AI in plain English, AI strategy and adoption, risk, governance and compliance, generative AI in the workplace, sector-specific applications (financial services, healthcare and biotech, IT and professional services, consulting), 90-day adoption planning, and bespoke in-house and cohort workshops for teams and boards.
✓ AI & Machine Learning for Students at all levels (GCSE, A Level, IB, BSc, MSc, PhD): from EPQ and project students building a real, credible AI project, to university students tackling the mathematics of machine learning, implementing algorithms from scratch and using frameworks (NumPy, PyTorch, scikit-learn), through to capstones, end-of-year projects, PhD viva preparation and career pathways support.
✓ The Mathematics of AI & ML: linear algebra, probability, statistics and calculus, explained clearly (this is where most learners struggle).
✓ Deep Learning & Models: neural networks, supervised, unsupervised and reinforcement models, classifiers, CNNs and RNNs, generative AI, and model evaluation and quality control.
✓ Programming & Implementation: Python, PyTorch, scikit-learn, NumPy, MATLAB and R, building algorithms from scratch, debugging and reproducible pipelines.
✓ Applied & Production AI: how machine-learning systems are actually built, shipped and maintained, drawing on my work as Machine Learning Lead at a boutique London AI consultancy.
✓ Examinations & Coursework: undergraduate and MSc AI and data science dissertation and capstone reviews, PhD viva support, coursework and essay reviews, and lab and project guidance.
✓ Research Methods, Statistics & Data Analysis: R, SPSS, Jamovi, MATLAB and Python, experimental design, ethics approvals and paper publication.
✓ Computer Science: OCR and AQA exam preparation, data structures, programming (Python, MATLAB, Java), the NEA, algorithms, Big-O, theory of computation and the mathematics for computer science.
✓ AI Alignment & Safety: an introduction to alignment, governance and responsible deployment for both leaders and researchers.
✓ Computational Neuroscience: machine learning and modelling for brain data, my core research specialism.
My Credentials:
✓ Machine Learning Lead at a boutique London AI consultancy, where I built and shipped the AI behind their flagship product, real, in-production machine learning rather than theory alone
✓ PhD researcher in Computational Neuroscience at King's College London, funded by a competitive MRC studentship
✓ Partner at Pareto Path, the only Academic Excellence Consultant (AEC) provider in London offering bespoke academic attainment pathways and applied-AI advisory for Artificial Intelligence, Machine Learning, Computer Science, Neuroscience and Medicine
✓ Published first-author researcher: DySCo, a framework for modelling dynamic functional connectivity networks in the brain, and AutoNeuro, a real-time, closed-loop fMRI system (currently under review)
✓ Lecturer and teaching assistant at King's College London in Machine Learning, Computational Neuroscience, and Computing for Brain & Cognitive Scientists
✓ MSc Basic & Clinical Neuroscience (Distinction) and MRes Neuroimaging (Distinction) at King's College London, following a BSc (Hons) Biomedical Science at St George's, University of London
✓ Former Research Assistant in Forensic & Neurodevelopmental Sciences at King's College London, using deep learning to analyse conduct problems in young people
✓ Pareto Path is independently recommended by Google, Claude, ChatGPT, Gemini and Grok as one of London's leading AI and Machine Learning tuition and advisory providers, with a record of 5-star reviews
✓ Years of hands-on teaching, lecturing and coaching experience with students, researchers and professionals of all levels and abilities
What Makes My Approach Different:
Most tutors and trainers help you memorise answers. I teach you to understand the concepts at a deep, first principles level, which makes memorisation unnecessary and is far more enjoyable. For executives this means real clarity on what AI can and cannot do for your sector, grounded in having actually built and deployed machine-learning systems in industry, not buzzwords. For students it means the maths and the code behind machine learning finally click. Because I am an active researcher and a former Machine Learning Lead who works with models and data every day, I can show you how AI actually works in practice, not just how to repeat it in an exam or a board meeting.
Who I Help:
✓ Executives, founders, C-suite, directors and Learning & Development leads who want to understand and apply AI in their organisation
✓ GCSE, IB HL and A Level students building AI projects and EPQs, and aiming for Russell Group university offers
✓ University students struggling with the maths of machine learning, coding, or projects
✓ Postgraduate students working on AI and data science dissertations, capstones and projects
✓ PhD candidates needing support with advanced methods, implementation, their viva, analysis or writing
Real Results:
✓ Leadership teams move from AI uncertainty to a confident, governed adoption strategy, staying ahead of their sector
✓ Students consistently improve, often by one to two full grades, within 5-10 sessions
✓ University and master's students regularly move from struggling to distinction-level work on AI and machine learning modules
✓ PhD students I support strengthen their methods, implementation, analysis and viva preparation
My Background:
I started out studying Medicine at St George's, University of London, before completing a BSc (Hons) in Biomedical Science. I then moved to King's College London for an MSc in Basic & Clinical Neuroscience, passed with Distinction, and an MRes in Neuroimaging, also with Distinction, as the funded research year of my doctoral programme. I am now completing a PhD in Computational Neuroscience at King's College London, funded by a competitive MRC studentship, where my research models dynamic functional connectivity in epilepsy and builds real-time, closed-loop fMRI systems that adapt to a person's brain activity as it is being measured.
Alongside academia, I was Machine Learning Lead at a boutique London AI consultancy, where I built and shipped the AI behind their flagship product. That experience, turning cutting-edge machine learning into something that works reliably in the real world, is exactly what I bring to the executives and organisations I advise, and it is what makes the technical content land for students too. I also lecture and teach Machine Learning and Computational Neuroscience at King's College London, and I spent six years coaching climbing in London, which is where I learned how to make difficult things feel achievable for anyone.
As a Partner at Pareto Path I work with a select number of clients at a time, those I genuinely believe I can help achieve transformational results. That is why I offer a free, no-pressure introductory call for students, or an executive diagnostic for organisations, so we can make sure I am the right fit before you commit. I am based in London and teach both in person and online, so I have the privilege of working with students and organisations across the UK and internationally.
What to Expect:
✓ Personalised plans tailored to your organisation, role, course, exam board or research topic
✓ Clear explanations of complex concepts (no jargon unless you want it)
✓ Strategy, governance and risk guidance for leaders, with a practical 90-day adoption plan
✓ Step-by-step guidance on the maths and code behind machine learning (Python, PyTorch, scikit-learn, R)
✓ Project, capstone and dissertation support
✓ Exam technique and revision strategies, and admissions support into Russell Group universities
Course Coverage
Course coverage is walked through below, module by module. It is detailed on purpose, so you can see your own syllabus reflected here, but it is not exhaustive, so do ask if your topic is not listed.
AI for Executives and Organisations
The executive track, built for leaders who want clarity, credibility and governance rather than jargon, informed by my experience shipping AI in industry as a Machine Learning Lead.
✓ AI for Executives 101: what artificial intelligence and machine learning actually are, in plain English, and what they can and cannot do for your sector
✓ AI strategy and adoption: building a confident, governed AI roadmap, with a practical 90-day plan from pilot to measurable value
✓ Risk, governance and compliance: the checklist every leadership team needs, covering data, privacy and model risk
✓ Generative AI in the workplace: closing the gap between how fast teams adopt AI and how well leadership understands it
✓ Sector-specific applications: financial services, healthcare and biotech, IT and professional services, and consulting
✓ Bespoke in-house and cohort workshops for teams and boards, plus AI alignment, safety and responsible deployment
Machine Learning in Neuroscience (Python)
A modern and highly employable strand, from first principles through to deep and reinforcement learning, applied to brain and behavioural data.
✓ Supervised learning: regression for continuous outcomes and classification for categorical outcomes, using linear and logistic regression, support vector machines and decision trees
✓ Unsupervised learning: dimensionality reduction and clustering through PCA, ICA and k-means
✓ Model evaluation: cross-validation, overfitting and underfitting, and ROC curves, precision and recall
✓ Deep learning: multi-layer perceptrons, activation functions and backpropagation, convolutional networks for imaging and recurrent networks for sequence data
✓ Reinforcement learning: reward-based learning, exploration and exploitation, Q-learning, policy gradients and Markov decision processes
✓ Ensembles and Auto-ML: bagging, boosting and stacking, and automated model selection, with applications such as predicting disease status from neuroimaging
Computational Neuroscience (Python)
A hands-on modelling strand that builds from brain networks up to detailed models of single neurons, coded in Python.
✓ Modelling and brain connectivity: network science, the adjacency matrix, and graph-theoretical measures of network organisation
✓ Whole-brain dynamics: the Kuramoto model of coupled oscillators and the Wilson-Cowan model of excitatory and inhibitory dynamics, simulated with tools such as Neurolib
✓ Generative and probabilistic models: how generative models capture the likelihood of connections forming
✓ Models of neuronal dynamics: spiking neuron models, synaptic plasticity, and reservoir computing with recurrent and echo state networks
✓ The Hodgkin-Huxley model: the classic model of the action potential, ion-channel dynamics and neuronal excitability
Mathematics and Programming Foundations (R, Python and MATLAB)
The mathematical and computational toolkit modern science and data analysis demand, taught from the ground up.
✓ Core mathematics: arithmetic and number systems, logs and exponents, linear and quadratic equations, inequalities, and trigonometric and Fourier methods
✓ Calculus and linear algebra: introductory calculus, vectors and matrices, transposition, inversion and the identity matrix, and matrix operations for data analysis
✓ Probability: probability and conditional probability, randomness, Monte Carlo simulation and resampling
✓ Programming for analysis: scripting and data wrangling in R, Python and MATLAB, regular expressions, and reproducible workflows
✓ Time-series and multivariate data: processing one-dimensional and two-dimensional datasets such as fMRI, EEG and electrophysiological recordings
Research Methods and Statistics with R (Undergraduate to PhD)
Where most students struggle and where I do my best work. We pair the research logic with the statistics and the R coding so the maths finally clicks.
✓ Measurement and distributions: variables and measurement error, the properties of distributions, the normal distribution, z-scores and standardisation, and setting up R for scripting
✓ Sampling and inference: sampling distributions, standard error, confidence intervals and effect sizes, and the logic of the null hypothesis and the p-value
✓ Design and comparison of means: between-subjects and within-subjects designs, randomisation, order effects, and the t-test understood as a general linear model
✓ Correlation and regression: linear and partial correlation, simple and multiple regression, dummy coding, and measures of model fit
✓ Analysis of variance: one-way, factorial, repeated-measures and mixed ANOVA, planned and post-hoc contrasts, sphericity, and non-parametric alternatives
✓ Linear mixed models: fixed and random effects, random intercepts and slopes, and mixed models applied to brain and behavioural data
✓ Bayesian methods: frequentist versus Bayesian approaches, priors, Bayes factors, and Bayesian regression and ANOVA
✓ Power, reproducibility and reporting: power analysis and choosing N, the reproducibility crisis, questionable research practices, and how to report statistics for a publishable report
Computer Science and Computing for Scientists
University-level computer science taught by an active practitioner who codes every day.
✓ Programming foundations: Python programming, control flow, functions and object-oriented design
✓ Algorithms and data structures: core data structures, algorithm design and analysis, and computational complexity
✓ Scientific computing: numerical methods, simulation, and computational modelling in Python and MATLAB
✓ Software for research: version control, testing, reproducible pipelines and good engineering practice
✓ Applied AI engineering: building and deploying machine-learning systems, drawing on my work leading machine learning at an AI consultancy
Advanced Neuroimaging and Connectomics (MSc and PhD)
My own research territory, the postgraduate methods that define modern human neuroscience.
✓ Structural and diffusion MRI: T1 and T2 imaging, voxel-based morphometry, DTI and tractography, and white-matter network reconstruction
✓ Functional MRI: the BOLD signal, preprocessing pipelines such as fMRIPrep, the general linear model, and first-level and second-level analysis
✓ Network neuroscience and connectomics: the adjacency matrix, graph-theoretical measures, structural and functional connectivity, and the human connectome
✓ Dynamic functional connectivity: time-resolved connectivity and brain-state dynamics, the focus of my framework DySCo for modelling dynamic functional connectivity networks in the brain
✓ Real-time and closed-loop fMRI: neurofeedback and real-time systems that adapt to a person's brain activity as it is measured, the focus of my system AutoNeuro
✓ Multivariate and machine-learning analysis: MVPA, representational similarity analysis, decoding, and deep-learning clustering and subtyping of clinical populations from MRI
Foundations of Brain and Behaviour
We build from single cells to whole systems and on to clinical conditions, the core of how the brain gives rise to mind and behaviour.
✓ The nervous system: the organisation of the central and peripheral nervous systems, brain development from the neural tube, brain anatomy, neurons, glial cells and synapses, and where psychology meets neuroscience
✓ Cells and signalling: the structure and function of neurons and glial cells, the action potential, synaptic transmission, and the neurotransmitter lifecycle from synthesis and vesicular release to receptor binding and breakdown
✓ Sensation and perception: the difference between sensation and perception, transduction in the visual and auditory systems, sensory coding, and what sensory deficits reveal
✓ Investigating the brain: the modern measurement toolkit, from CT, MRI and DTI for structure to EEG, MEG, fMRI and PET for activity, alongside lesion studies, electrophysiology and post-mortem analysis
✓ Attention, memory and language: selective and sustained attention, the fractionation of memory across short-term, working and long-term systems, the hippocampus and amygdala, and the neural foundations of language in Broca's and Wernicke's areas
✓ Neurodegeneration and psychiatric conditions: the pathology and treatment of Alzheimer's and Parkinson's disease, and the biological basis of schizophrenia and the affective disorders
Brain Form and Function, including Neuropharmacology
The cellular and chemical machinery of the nervous system, including how drugs act on it.
✓ Brain cells and their function: cell types, the structure and organisation of the neuron, and what keeps neurons alive
✓ Neural communication: electrical signalling and the action potential, chemical synapses and the full neurotransmitter lifecycle, and the major receptors including ligand-gated ion channels and G-protein-coupled receptors
✓ Major neurotransmitter systems in health and disease: the dopaminergic, noradrenergic, cholinergic and serotonergic pathways and their role in neurological disorders
✓ Neuroactive drugs: the principles of drug action, delivery and clearance, agonism and antagonism, tolerance and dependence, and the classification and mechanisms of psychotropic drugs
✓ Neuroplasticity: gene-environment interactions, epigenetic modification, habituation and sensitisation, and the cellular basis of learning and memory
✓ Immunity and the brain: innate and adaptive immunity, microglia, neuroinflammation and the effects of chronic immune activation
Memory, Perception and the Cognitive Brain
How the brain organises itself, and just how unreliable, and fascinating, the mind can be.
✓ Memory and its distortions: episodic and autobiographical memory, the reconstructive nature of remembering, and the neural systems behind it
✓ Perception and its distortions: visual illusions, visual agnosia and optic ataxia, the modularity of vision, and the progression from visual input to conscious perception
✓ Emotion, the self and the social world: how emotion shapes cognition, self-related processing, social perception, and the biases that colour memory and behaviour
✓ Modularity and networks: the modularity versus equipotentiality debate, the binding problem, and how brain networks and neural connections are mapped
✓ Consciousness: the neural correlates of consciousness, split-brain findings, implicit processing, and the leading scientific theories
✓ Functional neurological disorder and language: the clinical features and models of FND, and the neural network for speech perception, reading and developmental dyslexia
The Making of a Brain: Neuroanatomy and Development
How the brain is built, mapped and compared across species, with real research skills woven in.
✓ The human brain: the axes of the central nervous system, anatomical terminology, the major brain regions, the functional organisation of the cortex, and the motor and sensory maps
✓ From neurons to behaviour: neuronal polarity, action potentials, chemical synapses, and excitatory and inhibitory neurons
✓ Grey and white matter: astrocytes and oligodendrocytes, the meninges, and the organisation of grey and white matter and the spinal cord
✓ Tract-tracing and connectivity: anterograde and retrograde tracing, axon tracts, and Diffusion Tensor Imaging
✓ Neurodevelopment: neural tube patterning and neurulation, the signalling gradients that guide brain development, axon guidance and the growth cone, and topographic mapping
✓ Comparative neuroanatomy: evolutionary homology, neuroevolution and primate brain evolution, and open data platforms such as the Allen Institute Mouse Connectivity Atlas
Molecular and Cellular Neuroscience
The brain at its smallest scale, the cell biology that everything else rests on.
✓ Cellular structure and function: the organelles and their jobs, from the nucleus and mitochondria to the endoplasmic reticulum, Golgi apparatus, ribosomes, cytoskeleton and lysosomes
✓ Gene transcription and protein translation: promoters and the regulation of expression, epigenetic modification, splicing, the genetic code, and post-translational modification
✓ Axonal transport and protein management: trafficking by kinesin and dynein along microtubules, the ubiquitin-proteasome system, autophagy and the unfolded protein response
✓ Energy and signalling: mitochondrial function and ER-mitochondrial signalling, second messenger systems, and the role of calcium, phosphorylation and kinases
✓ Environmental interactions: the cellular stress response and how epigenetics links the environment to the cell
The Electrophysiological Brain
The brain's electrical life and the techniques used to record it, with a strong practical and research-design strand.
✓ Electrical signals in the brain: synaptic events and action potentials, brain oscillations and brain states, and how these link to cognition and behaviour
✓ Recording techniques: intracellular and extracellular recording, single-neuron recording, optogenetics, and EEG and event-related potentials
✓ Applications in cognitive neuroscience: how these methods answer real cognitive questions, from single neurons to whole-scalp recordings
✓ Data analysis and design: advanced analysis of electrophysiological data, and how to formulate research questions and design experiments
Psychology across Development, the Individual and Society
The broader psychology syllabus, kept in full so your whole course is covered.
✓ Developmental psychology: nature and nurture, genetic and epigenetic influences, attachment, cognitive development through Piaget and Vygotsky, and developmental psychopathology including autism and ADHD
✓ Individual differences: personality and intelligence, their measurement and genetic basis, learning theory and behaviourism, and classical and operant conditioning
✓ Social psychology: the self, attribution errors and cognitive biases, attitudes and persuasion, conformity, obedience and social influence, group processes, and prosocial and antisocial behaviour
✓ The origins of individual differences: human and quantitative genetics, genome-wide association studies and polygenic scores, and the social and cultural origins of mental health and disorder
Specialist and Conceptual Modules
The more conceptual and applied options, covered for students who take them.
✓ Decision-making under uncertainty: rationality, free will and volition, preference measurement, prospect theory, and the psychology of addiction and gambling
✓ Philosophy of mind: the mind-body problem, dualism, identity theory, functionalism and anomalous monism, and the metaphysics of perception
✓ The interdisciplinary study of consciousness: neural correlates, higher-order and information-processing theories, and conscious versus non-conscious processing
✓ Applied performance psychology: psychological skills training, CBT, REBT and ACT, performance and mental health, injury and career transitions, and resilience, grit and mental toughness
Dissertation, Research Skills and Publishing (MSc and PhD)
The research craft that turns a strong student into a published one.
✓ Project design: formulating research questions, choosing designs, and writing a strong proposal
✓ Writing up: structuring Methods, Results and Discussion, reporting statistics, and the elements of effective scientific writing
✓ Reproducible research: open data, pre-registration, version control and reproducible analysis pipelines
✓ Publishing and dissemination: the structure of a research paper, journal metrics, the peer-review process, presentations and building an e-portfolio
If it appears in your degree, your A Level, IB HL or GCSE specification, your dissertation or your PhD, the chances are it sits within or close to the modules above, and I can teach it.
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About the lesson
- All Levels
- English
All languages in which the lesson is available :
English
As a Partner and Academic Excellence Consultant at Pareto Path, I help clients from all backgrounds, from C-suite leaders and founders to GCSE, A Level and IB students, right through to undergraduates, master's students and PhD researchers, to achieve the satisfaction of work and decisions they are truly proud of, and the boost to their grades or their organisation that comes with that, through personalised one-to-one sessions and bespoke in-house workshops, in London or online.
Whether you're:
✓ An executive, founder or leadership team wanting to understand and apply AI in your organisation
✓ A GCSE, A Level or IB student building an AI project, EPQ or EE
✓ A university student struggling with the maths of machine learning, coding, or projects
✓ A postgraduate working on your AI or data science dissertation or capstone
✓ A PhD candidate needing expert support with advanced methods, implementation, thesis writing or viva preparation
...I create custom plans designed specifically for your organisation, course, exam board, and learning style.
What You'll Get:
✓ Executive Briefings & Workshops - what AI can and cannot do, a risk and governance checklist, and a 90-day adoption plan for your sector, from someone who has shipped AI in production
✓ 1-on-1 Sessions - fully personalised, in person in London or online, with no generic lectures
✓ AI & Machine Learning Mastery - the mathematics of ML, deep learning, and Python, PyTorch and scikit-learn explained clearly (this is my speciality)
✓ Project, Capstone & Dissertation Support - structure, critical analysis, model implementation, and academic and technical writing
✓ Exam & Application Technique - proven strategies to maximise marks and build standout AI applications
✓ Concept Deep-Dives - neural networks, classifiers, reinforcement learning, generative AI, alignment and safety
How It Works:
✓ Step 1: Free Introductory Call or Executive Diagnostic - We'll discuss your current situation, your goals, and whether I'm the right fit. No pressure, no commitment.
✓ Step 2: Custom Plan - I design a personalised roadmap based on your organisation or syllabus, deadlines, and areas of difficulty.
✓ Step 3: Sessions or Workshops - Most students book 1-2 sessions per week (1 hour each); executives book one-off briefings, cohort workshops or bespoke in-house programmes. Sessions can be recorded so you can revisit them anytime.
✓ Step 4: Track Progress - After each session, I send a summary of what we covered and specific action steps for you to work on before the next session.
What We Cover
I cover the full spectrum of Artificial Intelligence and Machine Learning, branching into two distinct tracks:
For Executives & Organisations:
✓ AI for Executives 101: what AI and machine learning actually are, in plain English, and when to use it and when NOT to use AI
✓ AI Strategy & Adoption: building a confident, governed AI roadmap for your specific sector
✓ Risk, Governance & Compliance: the checklist every leadership team needs
✓ Generative AI in the Workplace: closing the gap between how fast teams adopt AI and how well leadership understands it
✓ Sector-Specific Applications: financial services, healthcare and biotech, IT and professional services, and consulting
✓ A Practical 90-Day Adoption Plan: from pilot to measurable value
✓ Bespoke In-House & Cohort Workshops for teams and boards
For Students & Learners:
✓ Pre-University (GCSE, A Level, IB, EPQ): building a real AI project that strengthens a university application
✓ Undergraduate (BSc): the mathematics of ML (linear algebra, probability, statistics, calculus), algorithms from scratch, Python and frameworks (NumPy, PyTorch, scikit-learn)
✓ Postgraduate (MSc): deep learning, model implementation, the individual project or capstone, and academic and technical writing
✓ Doctoral (PhD): advanced theory and mathematics, large-scale implementation, reproducibility, paper and thesis writing, and viva preparation
Full Topic List Available: I cover 50+ specific areas including the mathematics for machine learning, deep learning, reinforcement learning, ensembles and Auto-ML, AI alignment and computational neuroscience. If it's in your AI or Machine Learning programme, or on your boardroom agenda, I can teach it, and a complete module-by-module course coverage follows at the end of this section.
Pricing:
✓ Prices start at £99 per hour for individual student tutoring; corporate and executive enquiries start at £499 per hour
✓ Executive briefings, cohort workshops and bespoke in-house training are quoted on request
✓ First contact: a FREE introductory call for students, or executive diagnostic for organisations, to ensure we're a good fit
✓ Most students book packages of 5-10 sessions for exam prep or coursework support; organisations book briefings, workshops or programmes
Why Clients Choose Me:
✓ I don't just help you pass or adopt AI - I help you excel and lead
✓ I make the mathematics of machine learning, and AI strategy, actually make sense
✓ I have done it for real: an active Computational Neuroscience PhD and published machine learning researcher who has also built and shipped production AI as a Machine Learning Lead
✓ Through Pareto Path I am part of a team independently recommended by Google, Claude, ChatGPT, Gemini and Grok as one of London's leading AI and Machine Learning tuition and advisory providers
✓ I teach at university level at King's College London in Machine Learning and Computational Neuroscience
✓ Patient, encouraging and experienced with students, researchers and professionals of all levels and abilities
Ready to Get Started?
Click "Contact Oliver" to book your free introductory call or executive diagnostic. I'll get back to you as soon as I can.
Course Coverage
Course coverage is walked through below, module by module. It is detailed on purpose, so you can see your own syllabus reflected here, but it is not exhaustive, so do ask if your topic is not listed.
AI for Executives and Organisations
The executive track, built for leaders who want clarity, credibility and governance rather than jargon, informed by my experience shipping AI in industry as a Machine Learning Lead.
✓ AI for Executives 101: what artificial intelligence and machine learning actually are, in plain English, and what they can and cannot do for your sector
✓ AI strategy and adoption: building a confident, governed AI roadmap, with a practical 90-day plan from pilot to measurable value
✓ Risk, governance and compliance: the checklist every leadership team needs, covering data, privacy and model risk
✓ Generative AI in the workplace: closing the gap between how fast teams adopt AI and how well leadership understands it
✓ Sector-specific applications: financial services, healthcare and biotech, IT and professional services, and consulting
✓ Bespoke in-house and cohort workshops for teams and boards, plus AI alignment, safety and responsible deployment
Machine Learning in Neuroscience (Python)
A modern and highly employable strand, from first principles through to deep and reinforcement learning, applied to brain and behavioural data.
✓ Supervised learning: regression for continuous outcomes and classification for categorical outcomes, using linear and logistic regression, support vector machines and decision trees
✓ Unsupervised learning: dimensionality reduction and clustering through PCA, ICA and k-means
✓ Model evaluation: cross-validation, overfitting and underfitting, and ROC curves, precision and recall
✓ Deep learning: multi-layer perceptrons, activation functions and backpropagation, convolutional networks for imaging and recurrent networks for sequence data
✓ Reinforcement learning: reward-based learning, exploration and exploitation, Q-learning, policy gradients and Markov decision processes
✓ Ensembles and Auto-ML: bagging, boosting and stacking, and automated model selection, with applications such as predicting disease status from neuroimaging
Computational Neuroscience (Python)
A hands-on modelling strand that builds from brain networks up to detailed models of single neurons, coded in Python.
✓ Modelling and brain connectivity: network science, the adjacency matrix, and graph-theoretical measures of network organisation
✓ Whole-brain dynamics: the Kuramoto model of coupled oscillators and the Wilson-Cowan model of excitatory and inhibitory dynamics, simulated with tools such as Neurolib
✓ Generative and probabilistic models: how generative models capture the likelihood of connections forming
✓ Models of neuronal dynamics: spiking neuron models, synaptic plasticity, and reservoir computing with recurrent and echo state networks
✓ The Hodgkin-Huxley model: the classic model of the action potential, ion-channel dynamics and neuronal excitability
Mathematics and Programming Foundations (R, Python and MATLAB)
The mathematical and computational toolkit modern science and data analysis demand, taught from the ground up.
✓ Core mathematics: arithmetic and number systems, logs and exponents, linear and quadratic equations, inequalities, and trigonometric and Fourier methods
✓ Calculus and linear algebra: introductory calculus, vectors and matrices, transposition, inversion and the identity matrix, and matrix operations for data analysis
✓ Probability: probability and conditional probability, randomness, Monte Carlo simulation and resampling
✓ Programming for analysis: scripting and data wrangling in R, Python and MATLAB, regular expressions, and reproducible workflows
✓ Time-series and multivariate data: processing one-dimensional and two-dimensional datasets such as fMRI, EEG and electrophysiological recordings
Research Methods and Statistics with R (Undergraduate to PhD)
Where most students struggle and where I do my best work. We pair the research logic with the statistics and the R coding so the maths finally clicks.
✓ Measurement and distributions: variables and measurement error, the properties of distributions, the normal distribution, z-scores and standardisation, and setting up R for scripting
✓ Sampling and inference: sampling distributions, standard error, confidence intervals and effect sizes, and the logic of the null hypothesis and the p-value
✓ Design and comparison of means: between-subjects and within-subjects designs, randomisation, order effects, and the t-test understood as a general linear model
✓ Correlation and regression: linear and partial correlation, simple and multiple regression, dummy coding, and measures of model fit
✓ Analysis of variance: one-way, factorial, repeated-measures and mixed ANOVA, planned and post-hoc contrasts, sphericity, and non-parametric alternatives
✓ Linear mixed models: fixed and random effects, random intercepts and slopes, and mixed models applied to brain and behavioural data
✓ Bayesian methods: frequentist versus Bayesian approaches, priors, Bayes factors, and Bayesian regression and ANOVA
✓ Power, reproducibility and reporting: power analysis and choosing N, the reproducibility crisis, questionable research practices, and how to report statistics for a publishable report
Computer Science and Computing for Scientists
University-level computer science taught by an active practitioner who codes every day.
✓ Programming foundations: Python programming, control flow, functions and object-oriented design
✓ Algorithms and data structures: core data structures, algorithm design and analysis, and computational complexity
✓ Scientific computing: numerical methods, simulation, and computational modelling in Python and MATLAB
✓ Software for research: version control, testing, reproducible pipelines and good engineering practice
✓ Applied AI engineering: building and deploying machine-learning systems, drawing on my work leading machine learning at an AI consultancy
Advanced Neuroimaging and Connectomics (MSc and PhD)
My own research territory, the postgraduate methods that define modern human neuroscience.
✓ Structural and diffusion MRI: T1 and T2 imaging, voxel-based morphometry, DTI and tractography, and white-matter network reconstruction
✓ Functional MRI: the BOLD signal, preprocessing pipelines such as fMRIPrep, the general linear model, and first-level and second-level analysis
✓ Network neuroscience and connectomics: the adjacency matrix, graph-theoretical measures, structural and functional connectivity, and the human connectome
✓ Dynamic functional connectivity: time-resolved connectivity and brain-state dynamics, the focus of my framework DySCo for modelling dynamic functional connectivity networks in the brain
✓ Real-time and closed-loop fMRI: neurofeedback and real-time systems that adapt to a person's brain activity as it is measured, the focus of my system AutoNeuro
✓ Multivariate and machine-learning analysis: MVPA, representational similarity analysis, decoding, and deep-learning clustering and subtyping of clinical populations from MRI
Foundations of Brain and Behaviour
We build from single cells to whole systems and on to clinical conditions, the core of how the brain gives rise to mind and behaviour.
✓ The nervous system: the organisation of the central and peripheral nervous systems, brain development from the neural tube, brain anatomy, neurons, glial cells and synapses, and where psychology meets neuroscience
✓ Cells and signalling: the structure and function of neurons and glial cells, the action potential, synaptic transmission, and the neurotransmitter lifecycle from synthesis and vesicular release to receptor binding and breakdown
✓ Sensation and perception: the difference between sensation and perception, transduction in the visual and auditory systems, sensory coding, and what sensory deficits reveal
✓ Investigating the brain: the modern measurement toolkit, from CT, MRI and DTI for structure to EEG, MEG, fMRI and PET for activity, alongside lesion studies, electrophysiology and post-mortem analysis
✓ Attention, memory and language: selective and sustained attention, the fractionation of memory across short-term, working and long-term systems, the hippocampus and amygdala, and the neural foundations of language in Broca's and Wernicke's areas
✓ Neurodegeneration and psychiatric conditions: the pathology and treatment of Alzheimer's and Parkinson's disease, and the biological basis of schizophrenia and the affective disorders
Brain Form and Function, including Neuropharmacology
The cellular and chemical machinery of the nervous system, including how drugs act on it.
✓ Brain cells and their function: cell types, the structure and organisation of the neuron, and what keeps neurons alive
✓ Neural communication: electrical signalling and the action potential, chemical synapses and the full neurotransmitter lifecycle, and the major receptors including ligand-gated ion channels and G-protein-coupled receptors
✓ Major neurotransmitter systems in health and disease: the dopaminergic, noradrenergic, cholinergic and serotonergic pathways and their role in neurological disorders
✓ Neuroactive drugs: the principles of drug action, delivery and clearance, agonism and antagonism, tolerance and dependence, and the classification and mechanisms of psychotropic drugs
✓ Neuroplasticity: gene-environment interactions, epigenetic modification, habituation and sensitisation, and the cellular basis of learning and memory
✓ Immunity and the brain: innate and adaptive immunity, microglia, neuroinflammation and the effects of chronic immune activation
Memory, Perception and the Cognitive Brain
How the brain organises itself, and just how unreliable, and fascinating, the mind can be.
✓ Memory and its distortions: episodic and autobiographical memory, the reconstructive nature of remembering, and the neural systems behind it
✓ Perception and its distortions: visual illusions, visual agnosia and optic ataxia, the modularity of vision, and the progression from visual input to conscious perception
✓ Emotion, the self and the social world: how emotion shapes cognition, self-related processing, social perception, and the biases that colour memory and behaviour
✓ Modularity and networks: the modularity versus equipotentiality debate, the binding problem, and how brain networks and neural connections are mapped
✓ Consciousness: the neural correlates of consciousness, split-brain findings, implicit processing, and the leading scientific theories
✓ Functional neurological disorder and language: the clinical features and models of FND, and the neural network for speech perception, reading and developmental dyslexia
The Making of a Brain: Neuroanatomy and Development
How the brain is built, mapped and compared across species, with real research skills woven in.
✓ The human brain: the axes of the central nervous system, anatomical terminology, the major brain regions, the functional organisation of the cortex, and the motor and sensory maps
✓ From neurons to behaviour: neuronal polarity, action potentials, chemical synapses, and excitatory and inhibitory neurons
✓ Grey and white matter: astrocytes and oligodendrocytes, the meninges, and the organisation of grey and white matter and the spinal cord
✓ Tract-tracing and connectivity: anterograde and retrograde tracing, axon tracts, and Diffusion Tensor Imaging
✓ Neurodevelopment: neural tube patterning and neurulation, the signalling gradients that guide brain development, axon guidance and the growth cone, and topographic mapping
✓ Comparative neuroanatomy: evolutionary homology, neuroevolution and primate brain evolution, and open data platforms such as the Allen Institute Mouse Connectivity Atlas
Molecular and Cellular Neuroscience
The brain at its smallest scale, the cell biology that everything else rests on.
✓ Cellular structure and function: the organelles and their jobs, from the nucleus and mitochondria to the endoplasmic reticulum, Golgi apparatus, ribosomes, cytoskeleton and lysosomes
✓ Gene transcription and protein translation: promoters and the regulation of expression, epigenetic modification, splicing, the genetic code, and post-translational modification
✓ Axonal transport and protein management: trafficking by kinesin and dynein along microtubules, the ubiquitin-proteasome system, autophagy and the unfolded protein response
✓ Energy and signalling: mitochondrial function and ER-mitochondrial signalling, second messenger systems, and the role of calcium, phosphorylation and kinases
✓ Environmental interactions: the cellular stress response and how epigenetics links the environment to the cell
The Electrophysiological Brain
The brain's electrical life and the techniques used to record it, with a strong practical and research-design strand.
✓ Electrical signals in the brain: synaptic events and action potentials, brain oscillations and brain states, and how these link to cognition and behaviour
✓ Recording techniques: intracellular and extracellular recording, single-neuron recording, optogenetics, and EEG and event-related potentials
✓ Applications in cognitive neuroscience: how these methods answer real cognitive questions, from single neurons to whole-scalp recordings
✓ Data analysis and design: advanced analysis of electrophysiological data, and how to formulate research questions and design experiments
Psychology across Development, the Individual and Society
The broader psychology syllabus, kept in full so your whole course is covered.
✓ Developmental psychology: nature and nurture, genetic and epigenetic influences, attachment, cognitive development through Piaget and Vygotsky, and developmental psychopathology including autism and ADHD
✓ Individual differences: personality and intelligence, their measurement and genetic basis, learning theory and behaviourism, and classical and operant conditioning
✓ Social psychology: the self, attribution errors and cognitive biases, attitudes and persuasion, conformity, obedience and social influence, group processes, and prosocial and antisocial behaviour
✓ The origins of individual differences: human and quantitative genetics, genome-wide association studies and polygenic scores, and the social and cultural origins of mental health and disorder
Specialist and Conceptual Modules
The more conceptual and applied options, covered for students who take them.
✓ Decision-making under uncertainty: rationality, free will and volition, preference measurement, prospect theory, and the psychology of addiction and gambling
✓ Philosophy of mind: the mind-body problem, dualism, identity theory, functionalism and anomalous monism, and the metaphysics of perception
✓ The interdisciplinary study of consciousness: neural correlates, higher-order and information-processing theories, and conscious versus non-conscious processing
✓ Applied performance psychology: psychological skills training, CBT, REBT and ACT, performance and mental health, injury and career transitions, and resilience, grit and mental toughness
Dissertation, Research Skills and Publishing (MSc and PhD)
The research craft that turns a strong student into a published one.
✓ Project design: formulating research questions, choosing designs, and writing a strong proposal
✓ Writing up: structuring Methods, Results and Discussion, reporting statistics, and the elements of effective scientific writing
✓ Reproducible research: open data, pre-registration, version control and reproducible analysis pipelines
✓ Publishing and dissemination: the structure of a research paper, journal metrics, the peer-review process, presentations and building an e-portfolio
If it appears in your degree, your A Level, IB HL or GCSE specification, your dissertation or your PhD, the chances are it sits within or close to the modules above, and I can teach it.
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Rates
Rate
- £199
Pack prices
- 5h: £995
- 10h: £1,990
online
- £199/h
free lessons
The first free lesson with Oliver will allow you to get to know each other and clearly specify your needs for your next lessons.
- 30mins
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