Mathematics / Computer Science Courses

A collection of some useful mathematics and computer science courses

Courses

Deep Learning for Computer Vision

Prof. Justin Johnson, University of Michigan, 2019

• Linear classifiers
• Stochastic gradient descent
• Fully-connected networks
• Convolutional networks
• Recurrent networks
• Attention and transformers
• Object detection
• Image segmentation
• Video classification
• Generative models (GANs, VAEs, autoregressive models)
• Reinforcement Learning

🎥 Lectures: https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r

High-Dimensional Probability

Roman Vershynin

🎥 Lectures: https://youtube.com/playlist?list=PLPjEEUWIWhQV7X6dXfrVP3w0KBBLBVJ0j
📔 Textbook : https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.html

Deep Unsupervised Learning

Prof. Pieter Abbeel, UC Berkeley, 2024

• Autoregressive Models
• Flow Models
• Latent Variable Models & Variational AutoEncoders (VAEs)
• Generative Adversarial Networks (GANs)
• Diffusion Models
• Self-Supervised Learning
• Large Language Models (LLMs)
• Generative Video
• Semisupervised Learning & Unsupervised Distribution Alignment
• Generative Modeling for Science
• Neural Radiance Fields
• Multimodal Models
• Parallelization

🎥 Lectures: https://youtube.com/playlist?list=PLwRJQ4m4UJjPIvv4kgBkvuuygrV3utU

Deep Generative Models

Prof. Stefano Ermon, Stanford University, 2023

• Autoregressive Models
• Maximum Likelihood Learning
• Variational AutoEncoders (VAEs)
• Normalizing Flows
• Generative Adversarial Networks (GANs)
• Energy Based Models (EBMs)
• Score Based Models
• Evaluation of Generative Models

🎥 Lectures: https://youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8
📔 Course page containing lecture notes: https://deepgenerativemodels.github.io

Geometric Deep Learning

African Master’s in Machine Intelligence, 2022

• High-Dimensional Learning
• Geometric Priors
• Graphs & Sets
• Grids
• Groups
• Geodesics & Manifolds
• Gauges

🎥 Lectures: https://youtube.com/playlist?list=PLn2-dEmQeTfSLXW8yXP4q_Ii58wFdxb3C
📔 webpage : https://geometricdeeplearning.com

Machine Learning with Graphs

Prof. Jure Leskovec, Stanford University, 2021

🎥 Lectures: https://youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn

Reinforcement Learning Theory

Prof. Csaba Szepesvári, University of Alberta, 2022

• MDP, Fundamental Theorem
• Value and Policy Iteration
• Local Planning
• Function Approximation
• Approximate Policy Iteration
• Planning Complexity, TensorPlan
• Lower Bound for API and POLITEX
• Policy Search
• Batch RL
• Online RL

🎥 Lectures: https://www.youtube.com/playlist?list=PLQCZ7_TRKVIzODPXorEyvhCk25TlcTANC
📔 webpage : https://rltheory.github.io/

Multi-Agent Reinforcement Learning

Dr. Stefano V. Albrecht, 2023

🎥 Lectures: https://www.youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2
📔 Textbook : http://www.marl-book.com/

Numerics of Machine Learning

University of Tübingen, 2023

• Numerical Linear Algebra
• Scaling Gaussian Processes
• Computation-aware Gaussian Processes
• State Space Models
• Solving Ordinary Differential Equations
• Probabilistic Numerical ODE Solvers
• Partial Differential Equations
• Monte Carlo
• Bayesian Quadrature
• Optimization for Deep Learning
• Second-order Optimization for Deep Learning
• Uncertainty in Deep Learning

🎥 Lectures : https://www.youtube.com/playlist?list=PL05umP7R6ij2lwDdj7IkuHoP9vHlEcH0s

Reinforcement Learning

Stanford, Prof. Emma Brunskill, 2024

• Introduction to Reinforcement Learning
• Tabular MDP planning
• Policy Evaluation
• Q learning and Function Approximation
• Policy Search
• Offline RL
• Exploration
• Multi-Agent Games
• Value Alignment

🎥 Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX

Model-Based Image and Signal Processing

Purdue University, Prof. Charles A. Bouman, 2020

• Probability
• Causal Gaussian Models
• Non-Causal Gaussian Models
• MAP with Gaussian Priors
• Non-Gaussian Markov Random Fields
• Non-Gaussian MAP
• Majorization
• Constrained Optimization
• Plug and Play
• EM Algorithm
• Markov Chains and HMMs
• General MRFs
• Stochastic Simulation
• MAP Segmentation

🎥 Lectures : https://www.youtube.com/playlist?list=PL3ZrjaBngMS0mTSoMsy7P6rTFSgsmsMw3
📔 Textbook: https://engineering.purdue.edu/~bouman/publications/FCI-book/

Convex Optimization

Stanford, Prof. Stephen Boyd, 2023

• Convex sets
• Convex functions
• Convex optimization problems
• Duality
• Approximation and fitting
• Statistical estimation
• Geometric problems
• Numerical linear algebra background
• Unconstrained minimization
• Equality constrained minimization
• Interior-point methods

🎥 Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rMJqxxviPa4AmDClvcbHi6h
📔 Textbook : https://stanford.edu/~boyd/cvxbook/

Learning in Games and Games in Learning

University of Pennsylvania, Prof. Aaron Roth, 2023

A mathematical course focusing on the interplay between game theory and machine learning: - Introduction to sequential learning - Halving algorithm - Follow the perturbed leader - Follow the regularized leader - Online convex optimization - Zero-sum games, Minimax theorem - Deriving a no regret learning algorithm - Correlated equilibrium, Swap regret - The adversary moves first framework - Multi-objective sequential learning

🎥 YouTube (24 lectures) : https://www.youtube.com/playlist?list=PLlIlherS4U2pfxYwB15nhzHEKF53xUl
📔 Lecture notes: https://mlgametheory.com/

Foundations of Reinforcement Learning

Princeton University, Prof. Chi Jin, 2024

A graduate level course on theoretical foundations of reinforcement learning:
- MDP basics and planning - Concentration inequalities, Martingale concentrations - Generative models, value iteration - Online RL, exploration, optimism - Minimax lower bound - Offline RL, pessimism - Policy optimization - Large state space, linear function approximation - General function approximation - Game theory and multiagent RL - Learning Markov games - Partial observable MDP

🎥 YouTube (22 lectures) : https://www.youtube.com/playlist?list=PLYXvCE1En13epbogBmgafC_Yyyk9oQogl
📔 Course page containing lecture notes: https://sites.google.com/view/cjin/teaching/ece524

Graduate Topics in Deep Learning Theory

Harvard Center of Mathematical Sciences and Applications, Dr. Eli Grigsby, 2024

A course on geometric aspects of deep learning theory: - The geometry and combinatorics of feedforward ReLU neural networks as piecewise linear function classes - Neural networks as universal approximators: discrete and non-discrete versions - The role of the superposition hypothesis in mechanistic interpretability of neural networks - Neural network architectures for sequence-to-sequence processing - Representing finite state automata using sequence-to-sequence architectures - Geometric distortion in deep networks and the importance of residual connections - Symmetries of overparameterized ReLU neural networks, optimization, and generalization - Algorithmic computation of topological invariants of decision boundaries/regions

🎥 YouTube: https://youtube.com/playlist?list=PL0NRmB0fnLJSEXFQHGF0q5JcedxTqK4AJ&si=G0rk4GBgywt6kypK
📔 Course page : https://sites.google.com/bc.edu/eli-grigsby/mt875-mechanistic-interpretability

Probabilistic Programming

University of British Columbia, Dr. Frank Wood, 2021

• Introduction to Model-Based Reasoning
• Graphical Models
• Inference, Learning, Monte Carlo, Sampling
• Markov Chain Monte Carlo
• First Order Probabilistic Programming Languages
• Graphical Model Compilation
• Graph-Based Inference
• Hamiltonian Monte Carlo
• Evaluation-based Inference
• Variational Inference
• Higher Order Probabilistic Programming Languages
• Amortized Inference / Guide Programs / Inference Compilation
• Reinforcement Learning as Inference
• Alternative Variational Bounds
• Reparametrization and Normalizing Flows

🎥 25 lectures on YouTube: https://youtube.com/playlist?list=PLRBUAK6di_6XlF7KAZBPRgcP0zD5sVXcN&si=9hjsRE1bav7vTqbG
📔 An Introduction to Probabilistic Programming: https://arxiv.org/abs/1809.10756

Learning and Reasoning with Bayesian Networks

UCLA, Prof. Adnan Darwiche

• Propositional Logic
• Probability Calculus: Beliefs and Hard Evidence, Soft Evidence
• Bayesian Networks: Syntax and Semantics
• Bayesian Networks: Independence and d-Separation
• Probabilistic Queries and their Complexity
• Building Bayesian Networks
• Inference by Variable Elimination
• The Jointree Algorithm
• Inference by Conditioning
• Arithmetic Circuits
• Loopy Belief Propagation
• Learning Parameters
• Learning Network Structure
• Bayesian Learning
• Causality
• Sensitivity Analysis
• Reasoning about Classifiers
• Explaining Classifiers

🎥 YouTube Playlist(32 lectures + 4 additional lectures on causality): https://youtube.com/playlist?list=PLlDG_zCuBub6ywAIrM1DfJp8xaeVjyvwx
📔 Textbook: Modeling and Reasoning with Bayesian Networks, Adnan Darwiche

Kernel methods in machine learning

ENS Paris-Saclay, Dr. Julien Mairal, Dr. Jean-Philippe Vert

• Positive definite kernels
• Reproducing Kernel Hilbert Space
• Smoothness functional, Kernel trick, Representer theorem
• Kernel ridge and logistic regression
• Large-margin classifiers, SVMs
• Unsupervised kernel methods
• Green, Mercer, Herglotz, Bochner and friends
• Kernels for graphs
• Multiple kernels learning
• Large-scale learning
• Deep kernel machines
• Kernels for probabilistic models
• Kernel mean embedding
• Characteristic kernels

🎥 YouTube Playlist (25 lectures): https://www.youtube.com/playlist?list=PLD93kGj6EdrkNj27AZMecbRlQ1SMkpo

Advanced Robotics

UC Berkeley, Prof. Pieter Abbeel, 2019

• Markov Decision Processes: Exact Methods
• Discretization of Continuous State Space MDPs
• Function Approximation
• LQR, iterative LQR, Differential Dynamic Programming
• Unconstrained Optimization
• Constrained Optimization
• Optimization-based Control
• Motion Planning
• Kalman Filtering, EKF, UKF
• Smoother, MAP, Maximum Likelihood, EM, KF parameter estimation
• Particle Filters
• Partially Observable MDPs
• Imitation Learning
• RL : Policy Gradients, Off-policy RL, Model-based RL
• Physics Simulation

🎥 YouTube Playlist (24 lectures) : https://youtube.com/playlist?list=PLwRJQ4m4UJjNBPJdt8WamRAt4XKc639wF&si=LrZXaiXafs6Qj07x

Statistical Machine Learning

Carnegie Mellon University, Prof. Larry Wasserman, 2016

• Function Spaces
• Concentration of Measure
• Linear Regression
• Non-Parametric Regression
• Trend Filtering
• Linear Classification
• Non-Parametric Classification
• Minimax Theory
• Non-Parametric Bayes
• Boosting
• Clustering
• Graphical Models
• Dimension Reduction
• Random Matrix Theory
• Differential Privacy

🎥 YouTube Playlist (24 lectures) : https://youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE&si=T5N31V-7ZPA_onXN

Optimization Methods for Machine Learning and Engineering

KIT(2020), Dr. Julius Pfrommer

• Introduction, Convexity and Gradient Descent
• Newton’s Method
• Inequality Constrained Optimization
• Equality Constrained Optimization
• Applications: Mechanical Design, Model-Predictive Control, Optimization in Finance
• Automatic Differentiation and Neural Networks
• Vector Spaces, Norms and the Projection Theorem
• Fast First-Order Optimization
• Duality and Primal-Dual Algorithms
• SVM and the Reproducing Kernel Hilbert Space
• Conic Programming
• Alternating Methods and the EM Algorithm
• Applications: Graph Problems, Computer Vision and Generalized Low-Rank Models
• Gradient-Free and Non-Convex Optimization

🎥 Lectures on YouTube : https://youtube.com/playlist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5&si=x3fYVDBXH7Y4TAmY

Probabilistic Reasoning & Machine Learning

TU Dortmund, Prof. Stefan Harmeling, 2022

🎥 Video lectures (28 sessions): https://youtube.com/playlist?list=PLzrCXlf6ypbzDYKDchKfM-I9s20mFCL0q&si=IuKihyN1QdWIuY8d

Parallel Computing and Scientific Machine Learning

MIT, Dr. Chris Rackauckas, 2021

• Getting Started with Julia
• Optimizing Serial Code
• Physics-Informed Neural Networks
• Introduction to Discrete Dynamical Systems
• The Basics of Single Node Parallel Computing
• Styles of Parallelism
• Ordinary Differential Equations
• Forward-Mode Automatic Differentiation
• Solving Stiff Ordinary Differential Equations
• Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems
• Differentiable Programming and Neural Differential Equations
• MPI for Distributed Computing
• Mathematics of ML and HPC
• GPU Computing
• Partial Differential Equations and Convolutional Neural Networks
• Probabilistic Programming
• Global Sensitivity Analysis
• Code Profiling and Optimization
• Uncertainty Programming and Generalized Uncertainty Quantification

🎥 Video Lectures: https://youtube.com/playlist?list=PLCAl7tjCwWyGjdzOOnlbGnVNZk0kB8VSa&si=-5MJhyhshyQ1SpcQ
📔 Lecture notes as an online book: https://book.sciml.ai/

Machine Learning and Bayesian Inference

University of Cambridge, Dr. Sean Holden

YouTube Playlist(15 lectures): https://youtube.com/playlist?list=PLdLk2RYEiAhp9Slj6FLCMXUv7Fi3V_Y&si=E-A3Igj-C3xrQJU2

Spectral Graph Theory

Iowa State University (2017), Prof. Steve Butler

🎥 Lectures (32 Sessions): https://www.youtube.com/playlist?list=PLi4h0n4UP8d9VGPqO8vLQga9ZApO65TLW
📔 Textbook: An Introduction to the Theory of Graph Spectra

Algorithmic Game Theory

Stanford, Prof. Tim Roughgarden

• Mechanism Design Basics
• Myerson's Lemma
• Algorithmic Mechanism Design
• Revenue-Maximizing Auctions
• Simple Near-Optimal Auctions
• VCG Mechanism
• Spectrum Auctions
• Beyond Quasi-Linearity
• Kidney Exchange, Stable Matching
• Selfish Routing and the POA
• Network Over-Provisioning
• Hierarchy of Equilibrium Concepts
• Smooth Games
• Best-Case and Strong Nash Equilibria
• Best-Response Dynamics
• No-Regret Dynamics
• Swap Regret; Minimax
• Pure NE and PLS-Completeness
• Mixed NE and PPAD-Completeness

🎥 Lectures: https://youtube.com/playlist?list=PLEGCF-WLh2RJBqmxvZ0ie-mleCFhi2N4&si=7r52RRF8miNr_N2

Advanced Mechanism Design

Stanford, Prof. Tim Roughgarden

• Ascending and Ex Post Incentive Compatible Mechanisms
• Unit-Demand Bidders and Walrasian Equilibria
• The Crawford-Knoer Auction
• The Clinching Auction
• The Gross Substitutes Condition
• Gross Substitutes-Welfare Maximization in Polynomial Time
• Submodular Valuations
• MIR and MIDR Mechanisms
• MIDR Mechanisms via Scaling Algorithms
• Coverage Valuations and Convex Rounding
• Undominated Implementations and the Shrinking Auction
• Bayesian Incentive-Compatibility
• Black Box Reductions
• The Price of Anarchy in Simple Auctions
• The Price of Anarchy of Bayes-Nash Equilibria
• The Price of Anarchy in First-Price Auctions
• Demand Reduction in Multi-Unit Auctions Revisited
• Beyond Smoothness and XOS Valuations
• Multi-Parameter Revenue-Maximization
• Interim Rules and Border’s Theorem
• Characterization of Revenue-Maximizing Auctions

🎥 Lectures: https://youtube.com/playlist?list=PLEGCF-WLh2RI77PL4gwLld_OU9Zh3TCX9

Algorithms and Uncertainty

Prof. Thomas Kesselheim

• Online Algorithms
• Online Learning Algorithms and Online Convex Optimization
• Markov Decision Processes
• Stochastic and Robust Optimization

🎥 Lectures: https://www.youtube.com/playlist?list=PLyzcvvgje7aDZRFMJZgaVgOW5t5KLvD1-

Information Geometry & its Applications

University of California, Prof. Melvin Leok, San Diego, 2022

🎥 Lectures: https://www.youtube.com/playlist?list=PLHZhjPByiV3L94AeJ9FcK1yrnRDOt3Vit

Advanced Scientific Computing

The University of Iceland, Prof. Ing Morris Riedel

High Performance Computing - Parallel Programming with MPI - Parallelization Fundamentals - Advanced MPI Techniques - Parallel Algorithms & Data Structures - Parallel Programming with OpenMP - Hybrid Programming & Patterns - Debugging & Profiling & Performance Analysis - Accelerators & Graphical Processing Units - Parallel & Scalable Machine & Deep Learning - HPC in Health & Neurosciences - Computational Fluid Dynamics & Finite Elements - Systems Biology & Bioinformatics - Molecular Systems & Material Sciences - Terrestrial Systems & Climate

🎥 2024 Lectures (ongoing): https://www.youtube.com/playlist?list=PLmJwSK7qduwVAnNfpueCgQqfchcSIEMg9
🎥 2023 Lectures: https://www.youtube.com/playlist?list=PLmJwSK7qduwUBwrFn3SY8vi4AYa2rVTWH
🎥 2022 Lectures: https://www.youtube.com/playlist?list=PLmJwSK7qduwWyqcSEB45HOyxq--z8njix

Deep Learning in Scientific Computing

ETH Zürich, Prof. Siddhartha Mishra, Dr. Benjamin Moseley, 2023

• Introduction to Deep Learning
• Physics-Informed Neural Networks
• Operator Learning
• Neural Operators
• Fourier Neural Operators and Convolutional Neural Operators
• Differentiable Physics

🎥 Course lectures: https://www.youtube.com/playlist?list=PLJkYEExhe7rYY5HjpIJbgo-tDZ3bIAqAm

Topology and Geometry

Prof. Tadashi Tokieda

🎥 Lectures: https://www.youtube.com/playlist?list=PLTBqohhFNBE_09L0i-lf3fYXF5woAbrzJ

Deep Reinforcement Learning

UC Berkeley, Prof. Sergey Levine

In addition to the standard RL topics, the course also includes: - RL and language models - Offline RL - Inverse RL - RL as probabilistic inference - Uncertainty and RL - Transfer learning and meta learning

🎥 Lectures(2021-2023): https://www.youtube.com/playlist?list=PLiWQOsE6TfVYGEGiAOMaOzzv41JfmPs

Information Theory

Harvard, Prof. Gregory Falkovich, 2022

🎥 Lectures: https://www.youtube.com/playlist?list=PLDEN2FPNHwVZKAFqfFl1b_NNAESTJwV9o
📔 Textbook (Physical Nature of Information): https://www.weizmann.ac.il/complex/falkovich/sites/complex.falkovich/files/uploads/PNI22.pdf

Bayesian Statistics

Virginia Tech, Prof. Scotland Leman, 2023

• Philosophy: What is probability?
• Fisher vs Neyman vs Jeffreys.
• The Likelihood Principle
• Basic Bayesian constructions: Likelihoods, priors and posteriors
• Exponential families and conjugate priors
• Asymptotics, Bayesian t-tests, mixture models, hierarchical modeling, etc..
• Bayesian sequential updating
• More on priors: Jeffreys, Reference, Objective, Subjective, etc...
• Simulation procedures: Gibbs, Metropolis, etc...
• Model Selection: Theory and Computational Approaches

🎥 Video lectures for the 2023 course and also lectures for the past semesters: https://www.youtube.com/@lemanlectures8611/videos
🎥 First lecture: https://youtu.be/vHAoj0Q5Auw?si=68ymPihUCaAmvvgK

Random Matrices and Machine Learning

Saarland University, Prof. Roland Speicher, 2023

🎥 Recorded videos (29 lectures): https://youtube.com/playlist?list=PLY11JnnnTUCabY4nc0hKptrd5qEWtLoo2&si=9HLbybgfW6pBss88

Computational Topology

University of Utah, Prof. Bei Wang, 2021

• Basic concepts (graphs, connected components, topological space, manifold, point cloud samples)
• Combinatorial structures on point cloud data (simplicial complexes)
• New techniques in dimension reduction (circular coordinates, etc.)
• Clustering (topology-based data partition, classification)
• Homology and persistent homology
• Topological signatures for classification
• Structural inference and reconstruction from data
• Topological algorithms for massive data
• Deep learning with TDA
• Multivariate and high-dimensional data analysis
• Topological data analysis for visualization (vector fields, topological structures)
• Practical applications of TDA

🎥 Playlist on YouTube (28 Lectures) : https://youtube.com/playlist?list=PLDZ6LA16SDbIvbgmCjcCuTA7mttfXjiec&si=FiadJKIdmKlJUIY7

Optimal Transport

Prof. Brittany Hamfeldt

🎥 Video Lectures: https://youtube.com/playlist?list=PLJ6garKOlK2qKVhRm6UwvcQ46wK-ciHbl&si=zeG5RCK_E04SRNww

Group Theory

Prof. Richard Borcherds

🎥 Lectures: https://www.youtube.com/@richarde.borcherds7998/playlists

Manifold Learning, Optimization and Information Geometry

Politecnico di Milano 2022

🎥 Lectures: https://youtube.com/playlist?list=PLvVaDdaHGtpesn2DHUo6ete-1pPhT1xzj&si=24WgTbFLChWMaJRx

Random Matrix Theory

King's College London, Dr Pierpaolo Vivo

🎥 Lectures : https://www.youtube.com/playlist?list=PLyHAvCibkccQEFYXdM6r8WG4GQULRKmRA

Topological Data Analysis

Colorado State University, Henry Adams, 2021

🎥 Videos (27 short lectures) : https://www.math.colostate.edu/~adams/teaching/dsci475spr2021/

Matrix Calculus for Machine Learning and Beyond

MIT, Prof. Alan Edelman, Prof. Steven G. Johnson, 2023

🎥 YouTube (8 lectures): https://youtube.com/playlist?list=PLUl4u3cNGP62EaLLH92E_VCN4izBKK6OE&si=rNoLocGXOEXBQjMH

Probabilistic Machine Learning

University of Tübingen, Dr. Philipp Hennig, 2023

• Reasoning Under Uncertainty
• Continuous Random Variables
• Exponential Families
• Gaussian Probability Distributions
• Parametric Regression
• Gaussian Processes
• Understanding Gaussian Processes
• GP Regression
• Understanding Kernels and Gaussian Processes
• The role of Linear Algebra in Gaussian Processes
• Computation and Inference
• Logistic Regression
• GP Classification
• Deep Learning
• Probabilistic Deep Learning
• Uncertainty in Deep Learning
• Uses of Uncertainty for Deep Learning
• Gauss-Markov Models
• Parameter Inference
• Variational Inference

🎥 Lectures (25 lectures): https://youtube.com/playlist?list=PL05umP7R6ij2YE8rRJSb-olDNbntAQBx&si=qivnfDBYjFOu1TOk
📔 Slides: https://github.com/philipphennig/ProbabilisticML

Discrete Differential Geometry

Carnegie Mellon Universit

🎥 Lectures : https://www.youtube.com/playlist?list=PL9_jI1bdZmz0hIrNCMQW1YmZysAiIYSSS

Applied Numerical Algorithms

MIT, Prof. Justin Solomon, 2023

🎥 Lectures : https://www.youtube.com/watch?v=Xt4p5gk24ss

Shape Analysis

MIT, Prof. Justin Solomon

🎥 Lectures : https://www.youtube.com/watch?v=VjyBp6PrvB4