Topic 22 – Introduction to Machine Learning

Why do I need to learn about machine learning?

Machine learning has solved many important difficult problems recently. A few of them include speech recognition, speech synthesis, image recognition, autonomous driving and chat bots.
Nowadays a key skill of software developer is the ability to use machine learning algorithms solve real-world problems.

What can I do after finishing learning about machine learning?

You will be to create software that could recognize car plate number from an image, identify probability of breast cancer for a patient.

That sounds useful! What should I do now?

Please audit
– this Machine Learning Specialization (Coursera) courses and
– this Applied Machine Learning in Python (Coursera) course.

At the same time, please read
– this Aurelien Geron (2022). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. O’Reilly Media book and
– this Brett Lantz (2019). Machine Learning with R – Expert Techniques for Predictive Modeling. Packt Publishing book, and
– this Michael A. Nielsen (2015). Neural Networks and Deep Learning. Determination Press book.

After that please watch
– this MIT 6.034 – Artificial Intelligence, Fall 2010 course (Readings).

After that, at the same time, please audit
– this Reinforcement Learning Specialization (Coursera) courses and read
– this Richard S. Sutton and Andrew G. Barto (2020). Reinforcement Learning. The MIT Press.

After that please read
– this Tom M. Mitchell (1997). Machine Learning. McGraw-Hill Education book, and
– this Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer book.

Supervised Learning Terminology Review:

  • Artificial Intelligence.
  • Machine Learning.
  • Deep Learning.
  • Linear Regression: Y = θX + Ε.
  • Cost Function measures how good/bad your model is.
  • Mean Square Error (MSE) measures the average of the squares of the errors.
  • Gradient Descent, Learning Rate.
  • Batch Gradient Descent.
  • The R-Squared Test measures the proportion of the total variance in the output (y) that can be explained by the variation in x. It can be used to evaluate how good a “fit” some model is on the given data.
  • Stochastic Gradient Descent.
  • Mini-Batch Gradient Descent.
  • Overfitting: machine learning model gives accurate predictions for training data but not for new data.
  • Regularization: Ridge Regression, Lasso Regression, Elastic Net, Early Stopping.
  • Logistic Regression.
  • Sigmoid Function.
  • Binary Cross Entropy Loss Function, Log Loss Function.
  • One Hot Encoding.
  • The Softmax function takes an N-dimensional vector of arbitrary real values and produces another N-dimensional vector with real values in the range (0, 1) that add up to 1.0.
  • Softmax Regression.
  • Support Vector Machines.
  • Decision Trees.
  • K-Nearest Neighbors.
  • McCulloch-Pitts Neuron.
  • Linear Threshold Unit with threshold T calculates the weighted sum of its inputs, and then outputs 0 if this sum is less than T, and 1 if the sum is greater than T.
  • Perceptron.
  • Activation Functions: Sigmoid, Hyperbolic Tangent, Rectified Linear Unit (ReLU).
  • Artificial Neural Networks.
  • Backpropagation.
  • Gradient Descent Optimization Algorithms: Momentum, Adagrad, Adadelta, RMSprop, Adam.
  • Regularization: Dropout.
  • The Joint Probability Table.
  • Bayesian Networks.
  • Naive Bayes Inference.

Unsupervised Learning Terminology Review:

  • K-Means.
  • Principal Component Analysis.
  • User-Based Collaborative Filtering.
  • Item-based Collaborative Filtering.
  • Matrix Factorization.

Reinforcement Learning Terminology Review:

  • k-armed Bandit Problem.
  • Bandit Algorithm.
  • Exponential Recency-Weighted Average.
  • Optimistic Initial Values.
  • Upper-Confidence-Bound Action Selection.
  • Agent.
  • World.
  • States, Terminal State.
  • Actions.
  • Rewards.
  • Markov Decision Processes: Agent (π) >> Action (a) >> World >> State (s), Reward >> Agent (π). Model: (current state, action, reward of current state, next state) = (s, a, R(s), s’).
  • Episodes.
  • Continuing Tasks.
  • Horizon (H): Number of time steps in each episode, can be infinite.
  • Expected Return: Sum of rewards from time step t to horizon H.
  • Discounted Return: Discounted sum of rewards from time step t to horizon H.
  • Discount Factor, Discount Rate: 0 ≤ γ ≤ 1.
  • Policy: Mapping from states to actions: π (s) = a or π (a|s) = P(aₜ=a|sₜ=s).
  • State Value Function – Vπ(s): The expected return starting from state s following policy π.
  • State-Action Value function, also known as the quality function – Qπ(s): The expected return starting from state , taking action , then following policy .
  • Bellman Equations.
  • Optimal Policies.
  • Optimal Value Functions.
  • Bellman Optimality Equations.
  • Policy Evaluation: (MDP, π) → Linear System Solver, Dynamic Programming → Vπ.
  • Iterative Policy Evaluation.
  • Policy Control, Policy Improvement.
  • Policy Improvement Theorem.
  • Greedy Policy.
  • Policy Iteration: (MDP) → Dynamic Programming → Vπ-optimal.
  • Value Iteration: MDP → (Qopt, πopt).
  • Asynchronous Dynamic Programming.
  • Generalized Policy Iteration.
  • Bootstrapping: Updating estimates on the basis of other estimates.
  • First-Visit Monte Carlo Prediction.
  • Exploring Starts.
  • Monte Carlo Control.
  • Model-Based Value Iteration.
  • Model-free Monte Carlo.
  • SARSA.
  • Function Approximation.
  • Continuous States.
  • Learning State Action Value function: Replay Buffer: 10,000 tuples most recent (s, a, R(s), s’). x = (s, a) → Q(θ) → y = R(s) + γmaxQ(s’, a’, θ). Loss = [R(s) + γmaxQ(s’, a’; θ)] − Q(s, a; θ).
  • Target Network: A separate neural network for generating the y targets. It has the same architecture as the original Q-Network. Loss = [R(s) + γmaxTargetQ(s’, a’; θ′)] − Q(s, a; θ). Every C time steps we will use the TargetQ-Network to generate the y targets and update the weights of the TargetQ-Network using the weights of the Q-Network.
  • Soft Updates: ← 0.001θ + 0.999, where and represent the weights of the target network and the current network, respectively.
  • Deep Reinforcement Learning, Deep Q-learning.
  • ϵ-greedy Policy: With probability 0.95, pick greedy action (exploitation). With probability 0.05, pick action randomly (exploration).

After finishing learning about machine learning please click Topic 23 – Introduction to Computer Vision to continue.