Канал: Frédéric Godin - Concordia

SL Prelim - Part1 (Mathematics and statistics preliminary material)

SL Prelim - Part1 (Mathematics and statistics preliminary material)

SL Chapter 3 Part4 (LASSO and the Bayesian viewpoint of shrinkage)

SL Chapter 3 Part4 (LASSO and the Bayesian viewpoint of shrinkage)

SL Chapter 3 Part3 (Shrinkage and ridge regressions)

SL Chapter 3 Part3 (Shrinkage and ridge regressions)

SL Chapter 3 Part2 (Concepts of model selection)

SL Chapter 3 Part2 (Concepts of model selection)

SL Chapter 3 Part1 (KNN and linear methods for regression)

SL Chapter 3 Part1 (KNN and linear methods for regression)

SL Chapter 2 Part1 (K-nearest neighbours + training/test sets)

SL Chapter 2 Part1 (K-nearest neighbours + training/test sets)

SL Chapter 1 - Overview of Statistical Learning

SL Chapter 1 - Overview of Statistical Learning

SL Prelim Part2 (Mathematics and statistics preliminary material)

SL Prelim Part2 (Mathematics and statistics preliminary material)

SL Chapter 9 Part1 (Feed-forward neural networks)

SL Chapter 9 Part1 (Feed-forward neural networks)

SL Chapter 7 Part2 (hierarchical clustering and distance metrics)

SL Chapter 7 Part2 (hierarchical clustering and distance metrics)

SL Chapter 7 Part1 (K-means clustering)

SL Chapter 7 Part1 (K-means clustering)

SL Chapter 6 Part3 (Bagging, Random Forests, Boosting, Variable importance)

SL Chapter 6 Part3 (Bagging, Random Forests, Boosting, Variable importance)

SL Chapter 6 Part2 (Classification trees, comparing decision trees with other predictive methods)

SL Chapter 6 Part2 (Classification trees, comparing decision trees with other predictive methods)

SL Chapter 6 Part1 (Decision trees, recursive binary splitting, cost complexity pruning)

SL Chapter 6 Part1 (Decision trees, recursive binary splitting, cost complexity pruning)

SL Chapter 5 Part3 (Logistic regression, Loss functions in classification)

SL Chapter 5 Part3 (Logistic regression, Loss functions in classification)

SL Chapter 5 Part2 (Linear and quadratic discriminant classifiers)

SL Chapter 5 Part2 (Linear and quadratic discriminant classifiers)

SL Chapter 5 Part1 (Classification problems, the Bayes classifier, K-nearest neighbours)

SL Chapter 5 Part1 (Classification problems, the Bayes classifier, K-nearest neighbours)

SL Chapter 4 Part4 (Local regression and Generalized Additive Models (GAMs) )

SL Chapter 4 Part4 (Local regression and Generalized Additive Models (GAMs) )

SL Chapter 4 Part3 (Piecewise polynominal, cubic splines and smoothing splines regressions)

SL Chapter 4 Part3 (Piecewise polynominal, cubic splines and smoothing splines regressions)

SL Chapter 4 Part1 (Generalized Linear Models)

SL Chapter 4 Part1 (Generalized Linear Models)

SL Chapter 9 Part2 (The backpropagation algorithm for neural network parameter estimation)

SL Chapter 9 Part2 (The backpropagation algorithm for neural network parameter estimation)

SL Chapter 10 Part2 (Support vector classifiers)

SL Chapter 10 Part2 (Support vector classifiers)

SL Chapter 10 Part1 (Maximal margin classifiers)

SL Chapter 10 Part1 (Maximal margin classifiers)

SL Chapter 8 Part2 (Principal component analysis (PCA) )

SL Chapter 8 Part2 (Principal component analysis (PCA) )

RL Chapter 5 Part3 (On-policy Monte-Carlo methods)

RL Chapter 5 Part3 (On-policy Monte-Carlo methods)

RL Chapter 5 Part2 (Monte-Carlo methods with exploring starts for control)

RL Chapter 5 Part2 (Monte-Carlo methods with exploring starts for control)

RL Chapter 5 Part1 (Monte-Carlo methods in Reinforcement Learning)

RL Chapter 5 Part1 (Monte-Carlo methods in Reinforcement Learning)

RL Chap4 Part2 (Dynamic Programming)

RL Chap4 Part2 (Dynamic Programming)

RL Chap4 Part1 (Dynamic Programming)

RL Chap4 Part1 (Dynamic Programming)

RL Chapter 3 Part3 (Bellman optimality equation and optimal policies)

RL Chapter 3 Part3 (Bellman optimality equation and optimal policies)

RL Chapter 3 Part2 (Markov Decision Processes, value function, Bellman equation)

RL Chapter 3 Part2 (Markov Decision Processes, value function, Bellman equation)

RL Chapter 2 Part3 (Upper confidence bounds, action preferences, contextual bandits)

RL Chapter 2 Part3 (Upper confidence bounds, action preferences, contextual bandits)

RL Chapter 2 Part2 (Multi-armed bandits: Recursive value estimates formulas, setting initial values)

RL Chapter 2 Part2 (Multi-armed bandits: Recursive value estimates formulas, setting initial values)

RL Chapter 2 Part1 (Multi-armed bandits problems, epsilon-greedy policies)

RL Chapter 2 Part1 (Multi-armed bandits problems, epsilon-greedy policies)

RL Chapter 13 Part2 (REINFORCE with baseline, actor-critic methods)

RL Chapter 13 Part2 (REINFORCE with baseline, actor-critic methods)

RL Chapter 9 Part4 (Construction of features within the linear approximation, neural networks)

RL Chapter 9 Part4 (Construction of features within the linear approximation, neural networks)

RL Chapter9 Part3 (State aggregation, linear approximations for the value function)

RL Chapter9 Part3 (State aggregation, linear approximations for the value function)

RL Chapter 9 Part2 (Semi-gradient estimation methods under value function approximation)

RL Chapter 9 Part2 (Semi-gradient estimation methods under value function approximation)

RL Chapter 9 Part1 (Approximation methods for the value function)

RL Chapter 9 Part1 (Approximation methods for the value function)

RL Chapter 7 Part2 (n-step off-policy learning)

RL Chapter 7 Part2 (n-step off-policy learning)

RL Chapter 7 Part1 (n-step TD methods)

RL Chapter 7 Part1 (n-step TD methods)

RL Chapter 6 Part4 (Expected SARSA, double learning and afterstates)

RL Chapter 6 Part4 (Expected SARSA, double learning and afterstates)

RL Chapter 6 Part3 (TD methods for control: SARSA, Q-learning)

RL Chapter 6 Part3 (TD methods for control: SARSA, Q-learning)

RL Chapter 6 Part1 (Temporal difference (TD) methods)

RL Chapter 6 Part1 (Temporal difference (TD) methods)

RL Chapter 5 Part5 (Off-policy Monte-Carlo methods, weighted importance sampling and recursions)

RL Chapter 5 Part5 (Off-policy Monte-Carlo methods, weighted importance sampling and recursions)

RL Chapter 5 Part4 (Importance sampling in off-policy Monte-Carlo methods of reinforcement learning)

RL Chapter 5 Part4 (Importance sampling in off-policy Monte-Carlo methods of reinforcement learning)

IQF Chapter 3 Part3 (Hedging methods based on Greek letters, volatility estimates, limitations)

IQF Chapter 3 Part3 (Hedging methods based on Greek letters, volatility estimates, limitations)

IQF Chapter 3 Part2 (Black-Scholes formula for option pricing, delta-hedging, Greek letters)

IQF Chapter 3 Part2 (Black-Scholes formula for option pricing, delta-hedging, Greek letters)

IQF Chapter 2 Part3 (Extension to the multi-period case continued)

IQF Chapter 2 Part3 (Extension to the multi-period case continued)

IQF Chapter 2 Part4 (Modifications to the binomial tree model, diversifiable versus systemic risk)

IQF Chapter 2 Part4 (Modifications to the binomial tree model, diversifiable versus systemic risk)

IQF Chapter 2 Part2 (Extension to the multi-period version of the binomial tree model)

IQF Chapter 2 Part2 (Extension to the multi-period version of the binomial tree model)

IQF Chapter 2 Part1 (Derivatives pricing in the one-period binomial market, replication)

IQF Chapter 2 Part1 (Derivatives pricing in the one-period binomial market, replication)

IQF Chapter 1 Part4 (Option price bounds, continuous dividends streams, transaction costs)

IQF Chapter 1 Part4 (Option price bounds, continuous dividends streams, transaction costs)

IQF Chapter 1 Part1 (Financial derivatives, forward & futures contracts, no-arbitrage pricing)

IQF Chapter 1 Part1 (Financial derivatives, forward & futures contracts, no-arbitrage pricing)

IQF Chapter 1 Part2 (Futures contracts, financial options)

IQF Chapter 1 Part2 (Futures contracts, financial options)

RL Chapter 1 - Overview of Reinforcement Learning

RL Chapter 1 - Overview of Reinforcement Learning

IQF Chap0 Part2 (Funding through equity approaches for corporations)

IQF Chap0 Part2 (Funding through equity approaches for corporations)

IQF Chapter 0 Part1 (Funding approaches for corporations. Debt related approaches)

IQF Chapter 0 Part1 (Funding approaches for corporations. Debt related approaches)

IQF Chapter 6 (Risk Measures)

IQF Chapter 6 (Risk Measures)

IQF Chapter 5 (CAPM and factor models)

IQF Chapter 5 (CAPM and factor models)

IQF Chapter 4 Part3 (Mean-variance portfolio optimization with risk-free asset, market portfolio)

IQF Chapter 4 Part3 (Mean-variance portfolio optimization with risk-free asset, market portfolio)

IQF Chapter 4 Part1 (Multi-asset framework for portfolio optimization, portfolio return moments)

IQF Chapter 4 Part1 (Multi-asset framework for portfolio optimization, portfolio return moments)

IQF Chapter 7 Part1 (The Efficient Market Hypothesis (EMH), market anomalies)

IQF Chapter 7 Part1 (The Efficient Market Hypothesis (EMH), market anomalies)

IQF Chapter 7 Part2 (Behavioral finance and biases)

IQF Chapter 7 Part2 (Behavioral finance and biases)

IQF Chapter 1 Part3 (Main uses of financial derivatives, option strategies and option price bounds)

IQF Chapter 1 Part3 (Main uses of financial derivatives, option strategies and option price bounds)

SL Chapter 2 Part2 (Cross-validation and sequential validation)

SL Chapter 2 Part2 (Cross-validation and sequential validation)

SL Chapter 4 Part2 (Generalized Linear Models and non-linear regression models)

SL Chapter 4 Part2 (Generalized Linear Models and non-linear regression models)

SL Chapter 8 Part1 (Principal Component Analysis (PCA) )

SL Chapter 8 Part1 (Principal Component Analysis (PCA) )

SL Chapter 5 Part4 (Loss functions, confusion matrices and ROC curves)

SL Chapter 5 Part4 (Loss functions, confusion matrices and ROC curves)

IQF Chapter 4 Part2 (Markowitz's Mean-variance portfolio optimization problem)

IQF Chapter 4 Part2 (Markowitz's Mean-variance portfolio optimization problem)

SL Chapter 10 Part3 (Support vector machines, multi-class implementations)

SL Chapter 10 Part3 (Support vector machines, multi-class implementations)

IQF Chapter 8 (Use of financial derivatives in actuarial science)

IQF Chapter 8 (Use of financial derivatives in actuarial science)

Mixed equity and fixed income fund model for segregated funds/variable annuities (ARC 2020)

Mixed equity and fixed income fund model for segregated funds/variable annuities (ARC 2020)

RL Chapter 3 Part1 (Markov Decision Processes)

RL Chapter 3 Part1 (Markov Decision Processes)

RL Chapter 6 Part2 (Convergence of TD methods, batch learning)

RL Chapter 6 Part2 (Convergence of TD methods, batch learning)

RL Chapter 13 Part1 (Policy gradient methods, policy gradient theorem, REINFORCE algorithm)

RL Chapter 13 Part1 (Policy gradient methods, policy gradient theorem, REINFORCE algorithm)

IQF Chapter 3 Part1 (Brownian Motion, and the Black-Scholes option pricing model)

IQF Chapter 3 Part1 (Brownian Motion, and the Black-Scholes option pricing model)