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MLcmore2023/readme.md

MLcmore2023

Session Lecture Lab (up to 2 hours) TA
D1-M 1. Introduction to AI and ML 1. Colab setup Bruce
2. Steps in ML project 2. Python refresh
3. Visualization: t-SNE
D1-A 1. Data preprocessing & EDA 1. Data prep: scaling, normalization, imputation Bruce
2. Performance evaluation 2. Feature selection
3. Model evaluation: classification, regression
D2-M 1. Model training 1. Model tune up Bruce
2. Bagging and Boosting
3. Supply chain example
D2-A 1. Supervised learning 1. Supervised learning Ramy
D3-M 1. Supervised learning 1. Supervised learning Nathan, Ramy
D3-A 1. Supervised learning 1. Supervised learning Ramy
D4-M 1. Deep learning 1. Deep learning Nathan, Ramy
D4-A 1. Deep learning 1. Deep learning Ramy
D5-M 1. Deep learning 1. Deep learning Nathan, Ramy
D5-A Evaluation Ramy
D6-M 1. Unsupervised learning 1. PCA Ramy
2. K-mean and cluster # optimization (elbow method)
D6-A 1. Unsupervised learning 1. Hierarchical clustering Ramy
2. Soft-clustering (expectation maximization)
D7-M 1. Markov decision process 1. Standard methods for MDP such as PI and VI Matin
• Formulation: transition probabilities, reward, …
• Policy evaluation
D7-A 1. MDP 1. PI and VI Matin
2. Monte Carlo method 2. Monte Carlo method
• Return computation
• Generalized Policy Iteration
D8-M 1. Tabular RL 1. Tabular RL: Q-learning, SARSA Matin
D8-A 1. Deep RL 1. Deep RL: DQN and others Matin
D9-M 1. Policy optimization 1. Policy optimization: REINFORCE, DPG, DDPG Matin
D9-A 1. Model-based RL, MARL 1. Dyna-Q, MARL Matin
D10-M 2. RL for Applications 1. DQN for optimal maintenance, Sim2Real for robotic control Matin
D10-A Evaluation Matin

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