Topics

Introduce the three problem domains: retail recommender systems, graph-based segmentation, and demand forecasting. Discuss how these tasks look on ‘small data’ (single-machine notebooks) versus ’large-scale’ settings with millions of users, items, or time series. Define basic ML concepts (features, targets, train/validation/test splits, empirical risk) and highlight what changes when data no longer fits in memory (data locality, communication cost, approximate vs exact algorithms).

Review supervised learning fundamentals (regression/classification, loss functions, overfitting) with emphasis on evaluation at scale: how full cross-validation becomes expensive and when single holdout or time-based splits are more appropriate. Contrast common metrics (MSE, MAE, MAPE, precision/recall, ranking metrics) and discuss computational cost of computing metrics on large datasets. Frame each project type formally: recommendation as ranking/utility estimation, segmentation as graph partitioning, forecasting as multi-series prediction. Use short quizzes around appropriate metric choice and evaluation strategies under compute constraints.

Introduce recommender systems as large, sparse matrix problems. Cover non-personalized methods (global popularity, item popularity within segments) and simple item item similarity from co-occurrence. Explain frequent itemset mining for market-basket analysis: definition of support, confidence, and association rules. Present FP-growth: how FP-trees compress transaction databases and avoid candidate explosion, and why it scales better than Apriori. Discuss scaling issues: building FP-trees per partition, memory limits, pruning by minimum support, and how these patterns can be used to derive item item edges or candidate sets in large retail systems. Include quick exercises on computing supports and small FP-trees.

Define graphs formally (nodes, edges, weighted/unweighted, directed/undirected) with examples from retail: product co-purchase graphs, user item bipartite graphs. Introduce degree, paths, and neighborhoods (ego-nets) as a way to analyze local structure. Cover connected components (weak/strong) and their interpretation in co-purchase or social graphs (e.g., isolated subgraphs vs a giant component). Discuss the computational challenges of processing large graphs: storing adjacency information across partitions, iteratively propagating labels (for components), and the difference between small networkx-style analyses and cluster-scale graph processing. Use small toy graphs for cut/degree/component quizzes.

Cover time-series fundamentals: trend, seasonality, residuals, stationarity. Discuss classical baseline models (na√Øve last-value, moving averages, simple exponential smoothing) and appropriate error metrics (RMSE, MAE, MAPE). Emphasize the “many-series” setting in retail (thousands of product √ó store series) and what breaks from single-series methods: memory/compute constraints, the need for shared models or hierarchical structures, and proper time-based evaluation (rolling or expanding windows). Highlight how naive random splits cause leakage in time-series and why backtesting protocols are needed at scale. Include quick exercises computing simple baselines and errors on short synthetic series.

Present low-rank matrix factorization for recommenders: representing user item interaction matrices as products of user and item latent factor matrices. Explain ALS-style objectives for implicit and explicit feedback, regularization, and how alternating optimization lends itself to distributed implementations (solving for user factors given item factors and vice versa). Discuss trade-offs between ALS and SGD at scale (synchronization, convergence, fault tolerance). Introduce sequential pattern mining via PrefixSpan: frequent subsequences, prefix-projected databases, and why PrefixSpan reduces database scans compared to older methods. Emphasize scaling issues: pattern explosion and the need for minimum support, maximum pattern length, and parallel prefix projections. Show how sequential patterns can inform next-item recommendation and session-level behavior modeling.