### DAY 1. Introduction to AI, ML, and LLMs

#### Machine Learning Flow

1. Training dataset
2. Algorithm/program execution
3. Pattern/rule extraction

Tasks:
- Object detection
- Value prediction
- Language tasks
- Face recognition

#### Language Models

- Next-token prediction
- Probability over continuations
- Token-by-token generation

#### Core Concepts

- Turing Test
- Maximum-likelihood estimation
- Count-and-divide baseline
- Markov assumption
- n-gram (unigram, bigram, n-gram)
- Neural language model

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### DAY 2. Basics of Machine Learning

#### Learning Types

- Supervised learning
- Unsupervised learning
- Reinforcement learning

#### Data and Features

- Data matrix
- Feature vector
- Histogram features
- Text features
- Image features

#### Text Representation

- Term-document matrix
- Bag of words
- tf-idf
- Neural embeddings

#### Vision Representation

- Convolution
- Kernel/filter
- Feature map
- 3x3 kernel
- Smoothing

#### Prediction Tasks

- Linear regression
- Logistic regression
- Multiclass one-vs-all
- Clustering
- K-means

#### Validation and Generalization

- Train/validation/test split
- Feature selection split
- Leave-one-out cross-validation
- Bias-variance tradeoff
- Overfitting
- Learning curves

#### Evaluation Metrics

- Accuracy
- Confusion matrix
- Precision
- Recall
- F1-score

---

### DAY 3. Data Preprocessing for NLP

#### NLP Tools

- Gensim
- spaCy
- IBM Watson
- MonkeyLearn
- TextBlob
- Stanford CoreNLP
- Google Cloud Natural Language API
- NLTK

#### NLTK Basics

- Package/module hierarchy
- Data-oriented classes
- Task-oriented classes
- token/probability/tree/CFG/tagger/parser/classifier/corpus modules

#### Token and Corpus Concepts

- Tokenization
- Word token vs word type
- Corpora (raw/annotated)
- Corpus access (raw text, words, sentences)

#### Preprocessing Steps

- Remove punctuation
- Lowercase
- Remove numbers
- Remove stop words
- Stop-word handling with scikit-learn

#### Morphology and Tagging

- Stemming
- Lemmatization
- POS tagging
- Tagset
- Default/regex/unigram/n-gram tagger

#### Modeling Bridge

- Shallow classification pipeline
- Deep classification pipeline
- Perceptron
- Multi-layer perceptron
- Backpropagation
- Computation graph

#### Neural Network Basics

- Capacity vs hidden size
- Regularization (weight penalty)
- Multi-class output
- Softmax
- Cross-entropy / log-likelihood

---

### DAY 4. Introduction to LLMs and Their Applications

#### Transformer Basics

- LLMs built on Transformers
- Attention-centered architecture
- Residual stream
- Feed-forward layer
- Layer normalization

#### Embeddings

- Static embeddings (word2vec)
- Contextual embeddings
- Context-dependent token meaning

#### Attention Mechanism

- Weighted context integration
- Dot-product similarity
- Left-to-right autoregressive attention
- Multi-head attention

#### Matrix Formulation

- Input matrix X [N x d]
- Q, K, V projections
- QK^T scores
- Scaling + softmax + V multiplication
- Parallel token computation

#### Generation Constraints

- Causal masking
- No future-token access
- O(N^2) attention complexity

#### Input/Output Heads

- Token embeddings
- Positional embeddings
- Token + position sum
- Language modeling head

#### Tokenization and Ecosystem

- BPE
- Hugging Face Transformers
- Hugging Face Hub
- Datasets
- Tokenizers
- AutoTokenizer / AutoModel
- Trainer

---

### DAY 5. Supervised Learning for NLP Tasks

#### Sentiment Analysis Scope

- Polarity classification
- Positive/Negative/Neutral labels
- Movie review example

#### Sentiment Components

- Holder (source)
- Target (aspect/entity)
- Attitude type
- Polarity and strength
- Sentence/document scope

#### Task Variants

- Binary polarity
- 1-5 rating
- Target/source/attitude extraction

#### Baseline Pipeline

- IMDB polarity setup
- Pang & Lee baseline line
- Feature extraction + classifier

#### Tokenization Issues

- HTML/XML markup
- Twitter markup
- Capitalization
- Dates/phone numbers
- Emoticons

#### Feature Engineering

- Adjectives vs all words
- Negation handling with `NOT_` prefix

#### Naive Bayes

- Label Y from text features X
- Prior / Likelihood / Posterior / Marginal
- Conditional independence assumption
- Multinomial NB
- Boolean Multinomial NB
- Normal vs Boolean comparison

#### Hard Cases

- Subtle wording
- Mixed sentiment
- Domain-specific language

#### LLM Classification with LangChain

- Zero-shot / few-shot classification
- PromptTemplate
- OutputParser
- LLMChain
- Runnable composition

---

### DAY 6. Advanced NLP Techniques

#### Sequence Modeling

- RNN
- Language modeling with RNN
- Sampling with language model
- Seq2Seq
- Conditional language modeling

#### GPT and BERT

- GPT (autoregressive)
- Attention masking
- Teacher forcing
- Generation
- BERT (bidirectional)
- Masked language modeling
- Transfer learning
- Fine-tuning
- BERT vs GPT

#### Embedding Topics

- Pre-LLM vs post-LLM embedding era
- Polysemy issue
- Vector space models
- Relatedness
- Word analogy
- Dense embedding learning
- Embedding layer (`torch.nn.Embedding`)

#### Reinforcement Learning Topics

- RL basics
- Agent/environment/reward
- Exploration vs exploitation
- Multi-armed bandit
- Greedy
- Epsilon-greedy
- 10-armed testbed
- Deep RL

---

### DAY 7. Language Generation with LLMs

#### RLHF Core

- Reinforcement Learning from Human Feedback
- Alignment-oriented generation
- Preference-based optimization

#### RLHF Pipeline

- Instruction-tuned base model
- Human feedback interface
- Preference/reward model
- RL fine-tuning

#### RL Optimization

- PPO
- Reward model signal
- KL penalty
- Combined rewards
- Feedback-training loop

#### Evaluation

- Human evaluation
- LLM-as-a-judge
- Leaderboards

#### Alignment and SFT

- Superficial Alignment Hypothesis
- InstructGPT (2022)
- Claude (2022)
- Llama 2 (2023)
- Supervised Fine-Tuning (SFT)
- Annotation scale and quality

---

### DAY 8. Ethical Considerations and Challenges in LLMs

#### Ethics Scope

- Ethics of AI/LLMs
- Dual-use risk
- Risk amplification

Examples:
- Richard Handl's Kitchen Reactor (2011)
- Mousepox experiment (2001)

#### Ethics Foundations

- What is ethics
- Ethical theories
- Trolley-style cases

#### Bias and Fairness

- Hiring-related bias cases
- COMPAS case
- Statistical bias
- Fairness perspectives
- Group fairness

#### Bias in LLMs

- Data bias
- Annotation/preference bias
- Objective/optimization bias
- Deployment feedback loops
- Synthetic data for bias discovery

#### Ethical Issue Categories

- Human agency and oversight
- Technical robustness and safety
- Privacy and data governance
- Transparency
- Diversity / non-discrimination / fairness
- Societal and environmental well-being
- Accountability

#### Security and Mitigation

- Prompt injection
- Fairness metrics
- Bias detection/mitigation tools
- MBIAS
- Governance controls

---

### DAY 9. Model Deployment and Integration in Applications

#### Reasoning Prompting

- Chain-of-Thought prompting
- Few-shot prompting
- Arithmetic reasoning results
- Ablation studies
- Robustness checks
- Common-sense and symbolic reasoning

#### Deployment Decisions

- API-based deployment
- Self-hosted deployment
- API vs self-host tradeoff
- OpenAI-compatible API importance

#### Inference Optimization

- Inference optimization
- Pricing model
- Token speed vs throughput
- Test-time compute scaling
- Prefix caching
- Prefix caching best practices

#### MLOps and LLMOps

- Typical ML pipeline
- Deployment gap
- MLOps principles
- Continuous workflows
- Version everything
- Automation
- Testing
- Reusability

#### Lifecycle and Operations

- MLOps tools
- LLM lifecycle in production
- Monitoring
- Versioning and rollback
- Cost/latency/throughput tracking
- Security and governance