Week 1

Course overview and structure

• Instructor highlights: course foundational aim, spanning classical ML (non-neural) early lectures and a later shift to DL-focused topics (GANs, reinforcement learning, transformers).
• Structure: first ~7–8 lectures on classical ML and math foundations; last ~5 lectures introduce deep learning, GANs, reinforcement learning, and transformers; a separate DL/NLP course is available next semester for deeper coverage.
• Emphasis: math and matrix operations underpin AI/ML; practical hands-on Python throughout the course.
• Tools emphasized: Python is essential; Google Colab recommended for simplicity and GPU access; Colab provides GPU power for practice; Colab is used for assignments to ensure consistent execution.
• Practical workflow: language/tools are aids, not substitutes for understanding; use tools to assist coding but learn the language and libraries to prompt tools effectively; avoid relying on GenAI for actual assignments; no AI tools during exams.
• Progression and outcomes: by course end, students should be proficient in classical ML techniques and familiar with reinforcement learning and GANs; DL details are reserved for the separate DL course.

Syllabus, office hours, and accessibility

• Attendance: mandatory for live sessions; if travel or other reasons, request recording.
• Office hours: updated to Wednesdays and Fridays, 07:30 PM Eastern; Zoom link provided on syllabus; topic tag for emails should be 8505 to aid prioritization.
• Contact: instructor emails with the 8505 tag; response typically within 24–48 hours.
• Syllabus edits: schedule times adjusted to fit students; changes posted on slides/Blackboard.
• Blackboard resources: slides, electronic reserves, tests, assignments, and exams housed on Blackboard with quick access to a Zoom-based office-hours link.

Python, Colab, and environment choices

• Python necessity: heavily Python-based course; a quick proficiency survey shows ~80% familiarity vs ~20% needing catch-up.
• Colab vs local: Colab used for uniformity and GPU access; local setups may have variability; Colab mitigates environment issues during assignments.
• Alternative environments: you can use other IDEs (e.g., VS Code) if you prefer, but Colab remains the recommended platform for coursework due to compute access and consistency.
• AI tooling caveat: tools like Copilot can assist coding, but learning the language and libraries is essential to prompt effectively and avoid over-reliance.
• Hardware costs: GPU usage via cloud platforms incurs cost; manage usage to avoid unnecessary expenses.

Textbooks, references, and readings

• Textbooks: treated as reference material; core content is what’s covered in class and assignments.
• Access to textbooks: department resources provide access; both free and paid options may be available.
• Reading guidance: course will indicate precursor chapters/pages; reading ahead is encouraged but not required; focus remains on covered lectures and assignments.
• Additional resources: recommended videos such as StatQuest for concepts like decision trees; instructor may provide additional links.

Course topics and structure: classical ML first, DL later

• Classical ML focus (pre-DL):
  • Core algorithms: SVMs, regression, decision trees, ensembles, dimensionality reduction, etc.
  • Emphasis on math foundations: matrix manipulations, linear algebra.
  • Practical math needs: concepts like learning rate, optimization basics, and regression/classification frameworks.
• Transition to DL topics (later lectures):
  • Introduction to deep learning, GANs, reinforcement learning, and transformers.
  • Deep learning emphasizes end-to-end learning from data with neural networks; the DL course covers these topics in greater depth.
• Real-world emphasis: ML as a driver for practical solutions; ML lifecycle and data-centric thinking are central.

What is AI, ML, DL? foundational concepts explained

• AI: umbrella term for systems that mimic human-like intelligence.
• ML: subset of AI where models learn from data to improve performance with experience; not explicitly programmed for every rule.
• DL: subset of ML using neural networks; can learn representations (features) from data without explicit feature engineering.
• Data science vs data engineering:
  • Data science focuses on extracting actionable insights from data.
  • Data engineering focuses on data collection, quality, and provisioning for data scientists.
• Practical takeaway: not all problems require DL; classical ML can be sufficient with proper data and feature engineering.

Data, data quality, and the ML pipeline

• Data quality is critical: garbage in, garbage out; data must be representative of the problem domain and properly cleaned.
• Data pipeline steps (high level):
  • Data collection and ingestion
  • Data cleaning and cleansing
  • Feature extraction/engineering
  • Splitting data into training, validation, and test sets
  • Model training and hyperparameter tuning
  • Evaluation and deployment
  • Monitoring and maintenance (drift handling and retraining)
• Training vs validation vs test sets:
  • Training data: used to fit the model.
  • Validation data: used to tune hyperparameters (hyperparameter tuning).
  • Test data: withheld to assess final model performance.
• Data split practice: splits are typically randomized by libraries; ensure the split maintains representative distribution and avoids data leakage.
• Overfitting vs underfitting:
  • Overfitting: model learns noise and training data too well; poor generalization.
  • Underfitting: model too simple to capture underlying patterns.
  • Techniques: regularization, early stopping, model complexity control, cross-validation, and proper data splitting.
• Normalization/standardization:
  • Feature scaling via standardization: $\hat{x} = \frac{x - \mu}{\sigma}$ to bring features onto similar scales.
  • Good practice to prevent certain features from dominating model learning.
• Feature engineering and discretization: transform continuous features, one-hot encode categorical features, log-transform skewed features, etc.
• Hyperparameters and search methods:
  • Hyperparameters are set by the programmer and not learned from data.
  • Tuning approaches: grid search, random search, Bayesian optimization.
• Ensembles:
  • Combine multiple models (e.g., Random Forest, SVM) and use voting/mixture for improved performance.
• Evaluation metrics (beyond accuracy):
  • Confusion matrix foundations, precision, recall, F1 score, ROC-AUC.
• Practical emphasis on data: the more data and the higher its quality, the better the learning; DL generally requires far more data than classical ML.
• Data ethics and responsibility: ensure data represents the problem domain and respects privacy; monitor models for drift after deployment.

The MNIST example and classification metrics

• MNIST dataset overview: 60,000 training images and 10,000 testing images, 28x28 grayscale for digits 0–9.
• Classification outcome concepts:
  • True positives (TP), true negatives (TN), false positives (FP), false negatives (FN).
  • Accuracy: $\text{Accuracy} = \frac{TP+TN}{TP+TN+FP+FN}$
  • Precision: $\text{Precision} = \frac{TP}{TP+FP}$
  • Recall: $\text{Recall} = \frac{TP}{TP+FN}$
  • F1 score: $F_1 = 2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}}$
  • Confusion matrix: diagonal elements represent correct classifications; off-diagonal elements represent misclassifications.
• Why accuracy alone can be misleading: imbalanced classes (e.g., rare digit) can yield high accuracy even with poor performance on minority class.
• ROC curve and AUC: area under ROC curve used to compare classifiers across different thresholds; higher AUC indicates better discrimination.
• False positives (Type I error) vs false negatives (Type II error): the relative cost depends on the use case (e.g., disease screening may prioritize minimizing false negatives).
• Precision-recall trade-offs and F1 score as a balanced metric when class distribution is imbalanced.
• ROC curves: concept of true positive rate (TPR) vs false positive rate (FPR); ideal classifier approaches the top-left corner (high TPR, low FPR).

Transformers, attention, and deeper DL topics (high-level)

• Transformers and attention: memory of the 2017 paper Attention Is All You Need; parallelizable computation and attention mechanisms to model word relationships.
• Use cases: NLP dominates transformer usage, but transformer architectures also adapt to images and other domains.
• Why transformers became dominant: scalable attention computations, unlocked by large-scale data and compute power; integration with GPUs/TPUs accelerates training.
• Note: detailed transformer internals will be covered in the DL/NLP course; this course provides a conceptual introduction.

Gartner hype cycle and technology context

• Gartner framework: technology evolution over time with phases like Innovation, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity.
• For trends like agentic AI, students should be aware of where technologies sit on the curve to anticipate robustness and ROI discussions.

Practical tips for exam preparation and course workflow

• Exams: midterms and final are multiple-choice; timing is typically 2 hours; cheat sheet allowed on one sheet of paper; exams will be conducted in-person in the course room, not online.
• Practice materials: a practice test is available in Blackboard; use it to verify camera setup and practice environment before the real exam.
• Homework vs exams: homework contributes a significant portion of the grade (e.g., ~30%); high-quality homework helps overall performance.
• Assignment workflow and timing:
  • Assignments open at 4:00 PM Eastern; students should work on a Python notebook provided in electronic reserves.
  • Students download the notebook/script, run it in Colab or their environment, and answer multiple-choice questions in the assignment.
  • Submissions are due before the next class meeting; scores are released after evaluation.
• Cheat sheet strategy: during exams, jot down the core formulas and concepts you’re most likely to forget; use it as a quick reference rather than re-deriving concepts.
• Collaboration and integrity: assignments are to be completed individually; network with peers for understanding but avoid sharing solutions.

Common questions and clarifications covered in the session

• Environment preferences: Colab is recommended for consistency and GPU access; other environments are acceptable if you can ensure reproducible results; practice tests should be run on your chosen setup to avoid surprises.
• The role of math in the course: while practical and applied, math (especially linear algebra and optimization) is central to understanding ML methods; the course emphasizes practical understanding and the math as needed to support intuition.
• Data considerations and sampling: ensure your train/validation/test splits are representative and random; visualize data distributions; guard against data leakage; consider dataset drift and model updates post-deployment.
• Teaching philosophy: focus on business problems driving the ML lifecycle rather than chasing novelty; use the data and problem statement to guide the model choice and evaluation.
• Break policy and pacing: a short break is scheduled during a long lecture; follow the instructor’s timing to maintain pace.

Quick study-ready summaries

• Key definitions:
  • AI, ML, DL, Data Science, Data Engineering
  • Supervised vs Unsupervised vs Semi-Supervised vs Reinforcement Learning
• Core formulas:
  • $\text{Accuracy} = \frac{TP+TN}{TP+TN+FP+FN}$
  • $\text{Precision} = \frac{TP}{TP+FP}$
  • $\text{Recall} = \frac{TP}{TP+FN}$
  • $F_1 = 2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}}$
  • Feature scaling: $\hat{x} = \frac{x - \mu}{\sigma}$
• Important practice:
  • Always keep the test set separate from training data to assess generalization.
  • Use validation data for hyperparameter tuning; avoid leaking test information.
  • Balance model complexity and data quantity to avoid underfitting and overfitting; consider regularization and early stopping.
• Exam readiness tips:
  • Review lecture notes and assignments thoroughly.
  • Practice with the Colab notebooks and be comfortable interpreting confusion matrices and related metrics.
  • Understand when to use DL vs classical ML given data size and problem complexity.

Closing reminders

• This course lays a strong foundation in classical AI/ML and introduces DL concepts with a focus on practical, data-driven problem solving.
• Active participation, timely submission of homeworks, and thoughtful preparation for midterms/finals are essential for success in this accelerated program.
• Maintain a strong connection with the cohort and instructor via the official channels (email with 8505 tag, Zoom office hours, Blackboard).