# 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?

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

– 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.

– 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.
• Mean Square Error (MSE) measures the average of the squares of the errors.
• 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.
• 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.
• 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.
• 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 $$s$$, taking action $$a$$, then following policy $$π$$.
• 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: $$θ′$$ $$← 0.001θ + 0.999θ′$$, where $$θ′$$ and $$θ$$ 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.